query: The query consists of users’ needs, leading to several research questions that span a paragraph. Each candidate passage is an abstract from a scientific paper. The objective of this information retrieval task is to identify the abstract that most effectively meets the user’s needs in the query : In our security company, we often deal with low-resolution images of faces captured from surveillance cameras, which makes it difficult to identify individuals accurately. We are looking for a solution that can generate high-resolution face images from these low-resolution inputs, especially when the input resolution is extremely low (e.g., only several hundred pixels) and the images are captured under uncontrolled settings with large pose and illumination variations. We hope the solution is based on a deep learning framework that can efficiently utilize global and local constraints defining a face. It would be beneficial if the framework had the following 2-step processes: one for holistic face reconstruction according to global constraints and another for enhancing face-specific details and enforcing local patch statistics. Additionally, we would like the deep network to be optimized using a loss function for super-resolution that combines reconstruction error with a learned face quality measured in an adversarial setting, resulting in robust visual results. We are interested in a solution that has been extensively tested in both controlled and uncontrolled setups and has demonstrated improvements over the current state of the art.

positive:
To learn disentangled representations of facial images, we present a Dual
Encoder-Decoder based Generative Adversarial Network (DED-GAN). In the proposed
method, both the generator and discriminator are designed with deep
encoder-decoder architectures as their backbones. To be more specific, the
encoder-decoder structured generator is used to learn a pose disentangled face
representation, and the encoder-decoder structured discriminator is tasked to
perform real/fake classification, face reconstruction, determining identity and
estimating face pose. We further improve the proposed network architecture by
minimising the additional pixel-wise loss defined by the Wasserstein distance
at the output of the discriminator so that the adversarial framework can be
better trained. Additionally, we consider face pose variation to be continuous,
rather than discrete in existing literature, to inject richer pose information
into our model. The pose estimation task is formulated as a regression problem,
which helps to disentangle identity information from pose variations. The
proposed network is evaluated on the tasks of pose-invariant face recognition
(PIFR) and face synthesis across poses. An extensive quantitative and
qualitative evaluation carried out on several controlled and in-the-wild
benchmarking datasets demonstrates the superiority of the proposed DED-GAN
method over the state-of-the-art approaches.

positive:
Recent learning-based approaches, in which models are trained by single-view
images have shown promising results for monocular 3D face reconstruction, but
they suffer from the ill-posed face pose and depth ambiguity issue. In contrast
to previous works that only enforce 2D feature constraints, we propose a
self-supervised training architecture by leveraging the multi-view geometry
consistency, which provides reliable constraints on face pose and depth
estimation. We first propose an occlusion-aware view synthesis method to apply
multi-view geometry consistency to self-supervised learning. Then we design
three novel loss functions for multi-view consistency, including the pixel
consistency loss, the depth consistency loss, and the facial landmark-based
epipolar loss. Our method is accurate and robust, especially under large
variations of expressions, poses, and illumination conditions. Comprehensive
experiments on the face alignment and 3D face reconstruction benchmarks have
demonstrated superiority over state-of-the-art methods. Our code and model are
released in https://github.com/jiaxiangshang/MGCNet.

positive:
Robust face reconstruction from monocular image in general lighting
conditions is challenging. Methods combining deep neural network encoders with
differentiable rendering have opened up the path for very fast monocular
reconstruction of geometry, lighting and reflectance. They can also be trained
in self-supervised manner for increased robustness and better generalization.
However, their differentiable rasterization based image formation models, as
well as underlying scene parameterization, limit them to Lambertian face
reflectance and to poor shape details. More recently, ray tracing was
introduced for monocular face reconstruction within a classic
optimization-based framework and enables state-of-the art results. However
optimization-based approaches are inherently slow and lack robustness. In this
paper, we build our work on the aforementioned approaches and propose a new
method that greatly improves reconstruction quality and robustness in general
scenes. We achieve this by combining a CNN encoder with a differentiable ray
tracer, which enables us to base the reconstruction on much more advanced
personalized diffuse and specular albedos, a more sophisticated illumination
model and a plausible representation of self-shadows. This enables to take a
big leap forward in reconstruction quality of shape, appearance and lighting
even in scenes with difficult illumination. With consistent face attributes
reconstruction, our method leads to practical applications such as relighting
and self-shadows removal. Compared to state-of-the-art methods, our results
show improved accuracy and validity of the approach.

positive:
There are many factors affecting visual face recognition, such as low
resolution images, aging, illumination and pose variance, etc. One of the most
important problem is low resolution face images which can result in bad
performance on face recognition. Most of the general face recognition
algorithms usually assume a sufficient resolution for the face images. However,
in practice many applications often do not have sufficient image resolutions.
The modern face hallucination models demonstrate reasonable performance to
reconstruct high-resolution images from its corresponding low resolution
images. However, they do not consider identity level information during
hallucination which directly affects results of the recognition of low
resolution faces. To address this issue, we propose a Face Hallucination
Generative Adversarial Network (FH-GAN) which improves the quality of low
resolution face images and accurately recognize those low quality images.
Concretely, we make the following contributions: 1) we propose FH-GAN network,
an end-to-end system, that improves both face hallucination and face
recognition simultaneously. The novelty of this proposed network depends on
incorporating identity information in a GAN-based face hallucination algorithm
via combining a face recognition network for identity preserving. 2) We also
propose a new face hallucination network, namely Dense Sparse Network (DSNet),
which improves upon the state-of-art in face hallucination. 3) We demonstrate
benefits of training the face recognition and GAN-based DSNet jointly by
reporting good result on face hallucination and recognition.

positive:
Despite generative adversarial networks (GANs) can hallucinate
photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they
cannot guarantee preserving the identities of hallucinated HR faces, making the
HR faces poorly recognizable. To address this problem, we propose a Siamese GAN
(SiGAN) to reconstruct HR faces that visually resemble their corresponding
identities. On top of a Siamese network, the proposed SiGAN consists of a pair
of two identical generators and one discriminator. We incorporate
reconstruction error and identity label information in the loss function of
SiGAN in a pairwise manner. By iteratively optimizing the loss functions of the
generator pair and discriminator of SiGAN, we cannot only achieve
photo-realistic face reconstruction, but also ensures the reconstructed
information is useful for identity recognition. Experimental results
demonstrate that SiGAN significantly outperforms existing face hallucination
GANs in objective face verification performance, while achieving
photo-realistic reconstruction. Moreover, for input LR faces from unknown
identities who are not included in training, SiGAN can still do a good job.

positive:
We present a deep learning approach for high resolution face completion with
multiple controllable attributes (e.g., male and smiling) under arbitrary
masks. Face completion entails understanding both structural meaningfulness and
appearance consistency locally and globally to fill in "holes" whose content do
not appear elsewhere in an input image. It is a challenging task with the
difficulty level increasing significantly with respect to high resolution, the
complexity of "holes" and the controllable attributes of filled-in fragments.
Our system addresses the challenges by learning a fully end-to-end framework
that trains generative adversarial networks (GANs) progressively from low
resolution to high resolution with conditional vectors encoding controllable
attributes.
  We design novel network architectures to exploit information across multiple
scales effectively and efficiently. We introduce new loss functions encouraging
sharp completion. We show that our system can complete faces with large
structural and appearance variations using a single feed-forward pass of
computation with mean inference time of 0.007 seconds for images at 1024 x 1024
resolution. We also perform a pilot human study that shows our approach
outperforms state-of-the-art face completion methods in terms of rank analysis.
The code will be released upon publication.

positive:
State-of-the-art face super-resolution methods employ deep convolutional
neural networks to learn a mapping between low- and high- resolution facial
patterns by exploring local appearance knowledge. However, most of these
methods do not well exploit facial structures and identity information, and
struggle to deal with facial images that exhibit large pose variations. In this
paper, we propose a novel face super-resolution method that explicitly
incorporates 3D facial priors which grasp the sharp facial structures. Our work
is the first to explore 3D morphable knowledge based on the fusion of
parametric descriptions of face attributes (e.g., identity, facial expression,
texture, illumination, and face pose). Furthermore, the priors can easily be
incorporated into any network and are extremely efficient in improving the
performance and accelerating the convergence speed. Firstly, a 3D face
rendering branch is set up to obtain 3D priors of salient facial structures and
identity knowledge. Secondly, the Spatial Attention Module is used to better
exploit this hierarchical information (i.e., intensity similarity, 3D facial
structure, and identity content) for the super-resolution problem. Extensive
experiments demonstrate that the proposed 3D priors achieve superior face
super-resolution results over the state-of-the-arts.

positive:
Over the past few decades, numerous attempts have been made to address the
problem of recovering a high-resolution (HR) facial image from its
corresponding low-resolution (LR) counterpart, a task commonly referred to as
face hallucination. Despite the impressive performance achieved by
position-patch and deep learning-based methods, most of these techniques are
still unable to recover identity-specific features of faces. The former group
of algorithms often produces blurry and oversmoothed outputs particularly in
the presence of higher levels of degradation, whereas the latter generates
faces which sometimes by no means resemble the individuals in the input images.
In this paper, a novel face super-resolution approach will be introduced, in
which the hallucinated face is forced to lie in a subspace spanned by the
available training faces. Therefore, in contrast to the majority of existing
face hallucination techniques and thanks to this face subspace prior, the
reconstruction is performed in favor of recovering person-specific facial
features, rather than merely increasing image quantitative scores. Furthermore,
inspired by recent advances in the area of 3D face reconstruction, an efficient
3D dictionary alignment scheme is also presented, through which the algorithm
becomes capable of dealing with low-resolution faces taken in uncontrolled
conditions. In extensive experiments carried out on several well-known face
datasets, the proposed algorithm shows remarkable performance by generating
detailed and close to ground truth results which outperform the
state-of-the-art face hallucination algorithms by significant margins both in
quantitative and qualitative evaluations.

positive:
Traditional methods for image-based 3D face reconstruction and facial motion
retargeting fit a 3D morphable model (3DMM) to the face, which has limited
modeling capacity and fail to generalize well to in-the-wild data. Use of
deformation transfer or multilinear tensor as a personalized 3DMM for
blendshape interpolation does not address the fact that facial expressions
result in different local and global skin deformations in different persons.
Moreover, existing methods learn a single albedo per user which is not enough
to capture the expression-specific skin reflectance variations. We propose an
end-to-end framework that jointly learns a personalized face model per user and
per-frame facial motion parameters from a large corpus of in-the-wild videos of
user expressions. Specifically, we learn user-specific expression blendshapes
and dynamic (expression-specific) albedo maps by predicting personalized
corrections on top of a 3DMM prior. We introduce novel constraints to ensure
that the corrected blendshapes retain their semantic meanings and the
reconstructed geometry is disentangled from the albedo. Experimental results
show that our personalization accurately captures fine-grained facial dynamics
in a wide range of conditions and efficiently decouples the learned face model
from facial motion, resulting in more accurate face reconstruction and facial
motion retargeting compared to state-of-the-art methods.

positive:
Face frontalization refers to the process of synthesizing the frontal view of
a face from a given profile. Due to self-occlusion and appearance distortion in
the wild, it is extremely challenging to recover faithful results and preserve
texture details in a high-resolution. This paper proposes a High Fidelity Pose
Invariant Model (HF-PIM) to produce photographic and identity-preserving
results. HF-PIM frontalizes the profiles through a novel texture warping
procedure and leverages a dense correspondence field to bind the 2D and 3D
surface spaces. We decompose the prerequisite of warping into dense
correspondence field estimation and facial texture map recovering, which are
both well addressed by deep networks. Different from those reconstruction
methods relying on 3D data, we also propose Adversarial Residual Dictionary
Learning (ARDL) to supervise facial texture map recovering with only monocular
images. Exhaustive experiments on both controlled and uncontrolled environments
demonstrate that the proposed method not only boosts the performance of
pose-invariant face recognition but also dramatically improves high-resolution
frontalization appearances.

positive:
Monocular 3D facial shape reconstruction from a single 2D facial image has
been an active research area due to its wide applications. Inspired by the
success of deep neural networks (DNN), we propose a DNN-based approach for
End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different
from recent works that reconstruct and refine the 3D face in an iterative
manner using both an RGB image and an initial 3D facial shape rendering, our
DNN model is end-to-end, and thus the complicated 3D rendering process can be
avoided. Moreover, we integrate in the DNN architecture two components, namely
a multi-task loss function and a fusion convolutional neural network (CNN) to
improve facial expression reconstruction. With the multi-task loss function, 3D
face reconstruction is divided into neutral 3D facial shape reconstruction and
expressive 3D facial shape reconstruction. The neutral 3D facial shape is
class-specific. Therefore, higher layer features are useful. In comparison, the
expressive 3D facial shape favors lower or intermediate layer features. With
the fusion-CNN, features from different intermediate layers are fused and
transformed for predicting the 3D expressive facial shape. Through extensive
experiments, we demonstrate the superiority of our end-to-end framework in
improving the accuracy of 3D face reconstruction.

positive:
Face Super-Resolution (SR) is a subfield of the SR domain that specifically
targets the reconstruction of face images. The main challenge of face SR is to
restore essential facial features without distortion. We propose a novel face
SR method that generates photo-realistic 8x super-resolved face images with
fully retained facial details. To that end, we adopt a progressive training
method, which allows stable training by splitting the network into successive
steps, each producing output with a progressively higher resolution. We also
propose a novel facial attention loss and apply it at each step to focus on
restoring facial attributes in greater details by multiplying the pixel
difference and heatmap values. Lastly, we propose a compressed version of the
state-of-the-art face alignment network (FAN) for landmark heatmap extraction.
With the proposed FAN, we can extract the heatmaps suitable for face SR and
also reduce the overall training time. Experimental results verify that our
method outperforms state-of-the-art methods in both qualitative and
quantitative measurements, especially in perceptual quality.

positive:
State-of-the-art deep neural network models have reached near perfect face
recognition accuracy rates on controlled high-resolution face images. However,
their performance is drastically degraded when they are tested with very
low-resolution face images. This is particularly critical in surveillance
systems, where a low-resolution probe image is to be matched with
high-resolution gallery images. super-resolution techniques aim at producing
high-resolution face images from low-resolution counterparts. While they are
capable of reconstructing images that are visually appealing, the
identity-related information is not preserved. Here, we propose an
identity-preserving end-to-end image-to-image translation deep neural network
which is capable of super-resolving very low-resolution faces to their
high-resolution counterparts while preserving identity-related information. We
achieved this by training a very deep convolutional encoder-decoder network
with a symmetric contracting path between corresponding layers. This network
was trained with a combination of a reconstruction and an identity-preserving
loss, on multi-scale low-resolution conditions. Extensive quantitative
evaluations of our proposed model demonstrated that it outperforms competing
super-resolution and low-resolution face recognition methods on natural and
artificial low-resolution face data sets and even unseen identities.

positive:
Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations.

positive:
We present a self-supervised learning approach to learning monocular 3D face
reconstruction with a pose guidance network (PGN). First, we unveil the
bottleneck of pose estimation in prior parametric 3D face learning methods, and
propose to utilize 3D face landmarks for estimating pose parameters. With our
specially designed PGN, our model can learn from both faces with fully labeled
3D landmarks and unlimited unlabeled in-the-wild face images. Our network is
further augmented with a self-supervised learning scheme, which exploits face
geometry information embedded in multiple frames of the same person, to
alleviate the ill-posed nature of regressing 3D face geometry from a single
image. These three insights yield a single approach that combines the
complementary strengths of parametric model learning and data-driven learning
techniques. We conduct a rigorous evaluation on the challenging AFLW2000-3D,
Florence and FaceWarehouse datasets, and show that our method outperforms the
state-of-the-art for all metrics.

positive:
Certain facial parts are salient (unique) in appearance, which substantially
contribute to the holistic recognition of a subject. Occlusion of these salient
parts deteriorates the performance of face recognition algorithms. In this
paper, we propose a generative model to reconstruct the missing parts of the
face which are under occlusion. The proposed generative model (SD-GAN)
reconstructs a face preserving the illumination variation and identity of the
face. A novel adversarial training algorithm has been designed for a bimodal
mutually exclusive Generative Adversarial Network (GAN) model, for faster
convergence. A novel adversarial "structural" loss function is also proposed,
comprising of two components: a holistic and a local loss, characterized by
SSIM and patch-wise MSE. Ablation studies on real and synthetically occluded
face datasets reveal that our proposed technique outperforms the competing
methods by a considerable margin, even for boosting the performance of Face
Recognition.

positive:
Face recognition performance has improved remarkably in the last decade. Much
of this success can be attributed to the development of deep learning
techniques such as convolutional neural networks (CNNs). While CNNs have pushed
the state-of-the-art forward, their training process requires a large amount of
clean and correctly labelled training data. If a CNN is intended to tolerate
facial pose, then we face an important question: should this training data be
diverse in its pose distribution, or should face images be normalized to a
single pose in a pre-processing step? To address this question, we evaluate a
number of popular facial landmarking and pose correction algorithms to
understand their effect on facial recognition performance. Additionally, we
introduce a new, automatic, single-image frontalization scheme that exceeds the
performance of current algorithms. CNNs trained using sets of different
pre-processing methods are used to extract features from the Point and Shoot
Challenge (PaSC) and CMU Multi-PIE datasets. We assert that the subsequent
verification and recognition performance serves to quantify the effectiveness
of each pose correction scheme.

positive:
Surveillance scenarios are prone to several problems since they usually
involve low-resolution footage, and there is no control of how far the subjects
may be from the camera in the first place. This situation is suitable for the
application of upsampling (super-resolution) algorithms since they may be able
to recover the discriminant properties of the subjects involved. While general
super-resolution approaches were proposed to enhance image quality for
human-level perception, biometrics super-resolution methods seek the best
"computer perception" version of the image since their focus is on improving
automatic recognition performance. Convolutional neural networks and deep
learning algorithms, in general, have been applied to computer vision tasks and
are now state-of-the-art for several sub-domains, including image
classification, restoration, and super-resolution. However, no work has
evaluated the effects that the latest proposed super-resolution methods may
have upon the accuracy and face verification performance in low-resolution
"in-the-wild" data. This project aimed at evaluating and adapting different
deep neural network architectures for the task of face super-resolution driven
by face recognition performance in real-world low-resolution images. The
experimental results in a real-world surveillance and attendance datasets
showed that general super-resolution architectures might enhance face
verification performance of deep neural networks trained on high-resolution
faces. Also, since neural networks are function approximators and can be
trained based on specific objective functions, the use of a customized loss
function optimized for feature extraction showed promising results for
recovering discriminant features in low-resolution face images.

positive:
The reconstruction of dense 3D models of face geometry and appearance from a
single image is highly challenging and ill-posed. To constrain the problem,
many approaches rely on strong priors, such as parametric face models learned
from limited 3D scan data. However, prior models restrict generalization of the
true diversity in facial geometry, skin reflectance and illumination. To
alleviate this problem, we present the first approach that jointly learns 1) a
regressor for face shape, expression, reflectance and illumination on the basis
of 2) a concurrently learned parametric face model. Our multi-level face model
combines the advantage of 3D Morphable Models for regularization with the
out-of-space generalization of a learned corrective space. We train end-to-end
on in-the-wild images without dense annotations by fusing a convolutional
encoder with a differentiable expert-designed renderer and a self-supervised
training loss, both defined at multiple detail levels. Our approach compares
favorably to the state-of-the-art in terms of reconstruction quality, better
generalizes to real world faces, and runs at over 250 Hz.

positive:
General image super-resolution techniques have difficulties in recovering
detailed face structures when applying to low resolution face images. Recent
deep learning based methods tailored for face images have achieved improved
performance by jointly trained with additional task such as face parsing and
landmark prediction. However, multi-task learning requires extra manually
labeled data. Besides, most of the existing works can only generate relatively
low resolution face images (e.g., $128\times128$), and their applications are
therefore limited. In this paper, we introduce a novel SPatial Attention
Residual Network (SPARNet) built on our newly proposed Face Attention Units
(FAUs) for face super-resolution. Specifically, we introduce a spatial
attention mechanism to the vanilla residual blocks. This enables the
convolutional layers to adaptively bootstrap features related to the key face
structures and pay less attention to those less feature-rich regions. This
makes the training more effective and efficient as the key face structures only
account for a very small portion of the face image. Visualization of the
attention maps shows that our spatial attention network can capture the key
face structures well even for very low resolution faces (e.g., $16\times16$).
Quantitative comparisons on various kinds of metrics (including PSNR, SSIM,
identity similarity, and landmark detection) demonstrate the superiority of our
method over current state-of-the-arts. We further extend SPARNet with
multi-scale discriminators, named as SPARNetHD, to produce high resolution
results (i.e., $512\times512$). We show that SPARNetHD trained with synthetic
data cannot only produce high quality and high resolution outputs for
synthetically degraded face images, but also show good generalization ability
to real world low quality face images.

positive:
Face hallucination is a domain-specific super-resolution problem that aims to
generate a high-resolution (HR) face image from a low-resolution~(LR) input. In
contrast to the existing patch-wise super-resolution models that divide a face
image into regular patches and independently apply LR to HR mapping to each
patch, we implement deep reinforcement learning and develop a novel
attention-aware face hallucination (Attention-FH) framework, which recurrently
learns to attend a sequence of patches and performs facial part enhancement by
fully exploiting the global interdependency of the image. Specifically, our
proposed framework incorporates two components: a recurrent policy network for
dynamically specifying a new attended region at each time step based on the
status of the super-resolved image and the past attended region sequence, and a
local enhancement network for selected patch hallucination and global state
updating. The Attention-FH model jointly learns the recurrent policy network
and local enhancement network through maximizing a long-term reward that
reflects the hallucination result with respect to the whole HR image. Extensive
experiments demonstrate that our Attention-FH significantly outperforms the
state-of-the-art methods on in-the-wild face images with large pose and
illumination variations.

positive:
In recent decades, 3D morphable model (3DMM) has been commonly used in
image-based photorealistic 3D face reconstruction. However, face images are
often corrupted by serious occlusion by non-face objects including eyeglasses,
masks, and hands. Such objects block the correct capture of landmarks and
shading information. Therefore, the reconstructed 3D face model is hardly
reusable. In this paper, a novel method is proposed to restore de-occluded face
images based on inverse use of 3DMM and generative adversarial network. We
utilize the 3DMM prior to the proposed adversarial network and combine a global
and local adversarial convolutional neural network to learn face de-occlusion
model. The 3DMM serves not only as geometric prior but also proposes the face
region for the local discriminator. Experiment results confirm the
effectiveness and robustness of the proposed algorithm in removing challenging
types of occlusions with various head poses and illumination. Furthermore, the
proposed method reconstructs the correct 3D face model with de-occluded
textures.

positive:
This paper studies the problem of blind face restoration from an
unconstrained blurry, noisy, low-resolution, or compressed image (i.e.,
degraded observation). For better recovery of fine facial details, we modify
the problem setting by taking both the degraded observation and a high-quality
guided image of the same identity as input to our guided face restoration
network (GFRNet). However, the degraded observation and guided image generally
are different in pose, illumination and expression, thereby making plain CNNs
(e.g., U-Net) fail to recover fine and identity-aware facial details. To tackle
this issue, our GFRNet model includes both a warping subnetwork (WarpNet) and a
reconstruction subnetwork (RecNet). The WarpNet is introduced to predict flow
field for warping the guided image to correct pose and expression (i.e., warped
guidance), while the RecNet takes the degraded observation and warped guidance
as input to produce the restoration result. Due to that the ground-truth flow
field is unavailable, landmark loss together with total variation
regularization are incorporated to guide the learning of WarpNet. Furthermore,
to make the model applicable to blind restoration, our GFRNet is trained on the
synthetic data with versatile settings on blur kernel, noise level,
downsampling scale factor, and JPEG quality factor. Experiments show that our
GFRNet not only performs favorably against the state-of-the-art image and face
restoration methods, but also generates visually photo-realistic results on
real degraded facial images.

positive:
Reconstructing the detailed geometric structure of a face from a given image
is a key to many computer vision and graphics applications, such as motion
capture and reenactment. The reconstruction task is challenging as human faces
vary extensively when considering expressions, poses, textures, and intrinsic
geometries. While many approaches tackle this complexity by using additional
data to reconstruct the face of a single subject, extracting facial surface
from a single image remains a difficult problem. As a result, single-image
based methods can usually provide only a rough estimate of the facial geometry.
In contrast, we propose to leverage the power of convolutional neural networks
to produce a highly detailed face reconstruction from a single image. For this
purpose, we introduce an end-to-end CNN framework which derives the shape in a
coarse-to-fine fashion. The proposed architecture is composed of two main
blocks, a network that recovers the coarse facial geometry (CoarseNet),
followed by a CNN that refines the facial features of that geometry (FineNet).
The proposed networks are connected by a novel layer which renders a depth
image given a mesh in 3D. Unlike object recognition and detection problems,
there are no suitable datasets for training CNNs to perform face geometry
reconstruction. Therefore, our training regime begins with a supervised phase,
based on synthetic images, followed by an unsupervised phase that uses only
unconstrained facial images. The accuracy and robustness of the proposed model
is demonstrated by both qualitative and quantitative evaluation tests.

negative:
Recently, a lot of attention has been focused on the incorporation of 3D data
into face analysis and its applications. Despite providing a more accurate
representation of the face, 3D facial images are more complex to acquire than
2D pictures. As a consequence, great effort has been invested in developing
systems that reconstruct 3D faces from an uncalibrated 2D image. However, the
3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is
needed to restrict the solutions space. In this work, we review 3D face
reconstruction methods proposed in the last decade, focusing on those that only
use 2D pictures captured under uncontrolled conditions. We present a
classification of the proposed methods based on the technique used to add prior
knowledge, considering three main strategies, namely, statistical model
fitting, photometry, and deep learning, and reviewing each of them separately.
In addition, given the relevance of statistical 3D facial models as prior
knowledge, we explain the construction procedure and provide a list of the most
popular publicly available 3D facial models. After the exhaustive study of
3D-from-2D face reconstruction approaches, we observe that the deep learning
strategy is rapidly growing since the last few years, becoming the standard
choice in replacement of the widespread statistical model fitting. Unlike the
other two strategies, photometry-based methods have decreased in number due to
the need for strong underlying assumptions that limit the quality of their
reconstructions compared to statistical model fitting and deep learning
methods. The review also identifies current challenges and suggests avenues for
future research.

negative:
Face super-resolution methods usually aim at producing visually appealing
results rather than preserving distinctive features for further face
identification. In this work, we propose a deep learning method for face
verification on very low-resolution face images that involves
identity-preserving face super-resolution. Our framework includes a
super-resolution network and a feature extraction network. We train a VGG-based
deep face recognition network (Parkhi et al. 2015) to be used as feature
extractor. Our super-resolution network is trained to minimize the feature
distance between the high resolution ground truth image and the super-resolved
image, where features are extracted using our pre-trained feature extraction
network. We carry out experiments on FRGC, Multi-PIE, LFW-a, and MegaFace
datasets to evaluate our method in controlled and uncontrolled settings. The
results show that the presented method outperforms conventional
super-resolution methods in low-resolution face verification.

negative:
Prior knowledge of face shape and structure plays an important role in face
inpainting. However, traditional face inpainting methods mainly focus on the
generated image resolution of the missing portion without consideration of the
special particularities of the human face explicitly and generally produce
discordant facial parts. To solve this problem, we present a domain embedded
multi-model generative adversarial model for inpainting of face images with
large cropped regions. We firstly represent only face regions using the latent
variable as the domain knowledge and combine it with the non-face parts
textures to generate high-quality face images with plausible contents. Two
adversarial discriminators are finally used to judge whether the generated
distribution is close to the real distribution or not. It can not only
synthesize novel image structures but also explicitly utilize the embedded face
domain knowledge to generate better predictions with consistency on structures
and appearance. Experiments on both CelebA and CelebA-HQ face datasets
demonstrate that our proposed approach achieved state-of-the-art performance
and generates higher quality inpainting results than existing ones.

negative:
Given a monocular face image as input, 3D face geometry reconstruction aims
to recover a corresponding 3D face mesh. Recently, both optimization-based and
learning-based face reconstruction methods have taken advantage of the emerging
differentiable renderer and shown promising results. However, the
differentiable renderer, mainly based on graphics rules, simplifies the
realistic mechanism of the illumination, reflection, \etc, of the real world,
thus cannot produce realistic images. This brings a lot of domain-shift noise
to the optimization or training process. In this work, we introduce a novel
Generative Adversarial Renderer (GAR) and propose to tailor its inverted
version to the general fitting pipeline, to tackle the above problem.
Specifically, the carefully designed neural renderer takes a face normal map
and a latent code representing other factors as inputs and renders a realistic
face image. Since the GAR learns to model the complicated real-world image,
instead of relying on the simplified graphics rules, it is capable of producing
realistic images, which essentially inhibits the domain-shift noise in training
and optimization. Equipped with the elaborated GAR, we further proposed a novel
approach to predict 3D face parameters, in which we first obtain fine initial
parameters via Renderer Inverting and then refine it with gradient-based
optimizers. Extensive experiments have been conducted to demonstrate the
effectiveness of the proposed generative adversarial renderer and the novel
optimization-based face reconstruction framework. Our method achieves
state-of-the-art performances on multiple face reconstruction datasets.

negative:
In this paper, we propose an effective face completion algorithm using a deep
generative model. Different from well-studied background completion, the face
completion task is more challenging as it often requires to generate
semantically new pixels for the missing key components (e.g., eyes and mouths)
that contain large appearance variations. Unlike existing nonparametric
algorithms that search for patches to synthesize, our algorithm directly
generates contents for missing regions based on a neural network. The model is
trained with a combination of a reconstruction loss, two adversarial losses and
a semantic parsing loss, which ensures pixel faithfulness and local-global
contents consistency. With extensive experimental results, we demonstrate
qualitatively and quantitatively that our model is able to deal with a large
area of missing pixels in arbitrary shapes and generate realistic face
completion results.

negative:
This paper addresses 2 challenging tasks: improving the quality of low
resolution facial images and accurately locating the facial landmarks on such
poor resolution images. To this end, we make the following 5 contributions: (a)
we propose Super-FAN: the very first end-to-end system that addresses both
tasks simultaneously, i.e. both improves face resolution and detects the facial
landmarks. The novelty or Super-FAN lies in incorporating structural
information in a GAN-based super-resolution algorithm via integrating a
sub-network for face alignment through heatmap regression and optimizing a
novel heatmap loss. (b) We illustrate the benefit of training the two networks
jointly by reporting good results not only on frontal images (as in prior work)
but on the whole spectrum of facial poses, and not only on synthetic low
resolution images (as in prior work) but also on real-world images. (c) We
improve upon the state-of-the-art in face super-resolution by proposing a new
residual-based architecture. (d) Quantitatively, we show large improvement over
the state-of-the-art for both face super-resolution and alignment. (e)
Qualitatively, we show for the first time good results on real-world low
resolution images.

negative:
Several factors contribute to the appearance of an object in a visual scene,
including pose, illumination, and deformation, among others. Each factor
accounts for a source of variability in the data, while the multiplicative
interactions of these factors emulate the entangled variability, giving rise to
the rich structure of visual object appearance. Disentangling such unobserved
factors from visual data is a challenging task, especially when the data have
been captured in uncontrolled recording conditions (also referred to as
"in-the-wild") and label information is not available.
  In this paper, we propose the first unsupervised deep learning method (with
pseudo-supervision) for disentangling multiple latent factors of variation in
face images captured in-the-wild. To this end, we propose a deep latent
variable model, where the multiplicative interactions of multiple latent
factors of variation are explicitly modelled by means of multilinear (tensor)
structure. We demonstrate that the proposed approach indeed learns disentangled
representations of facial expressions and pose, which can be used in various
applications, including face editing, as well as 3D face reconstruction and
classification of facial expression, identity and pose.

negative:
Deep neural networks (DNNs) trained on large-scale datasets have recently
achieved impressive improvements in face recognition. But a persistent
challenge remains to develop methods capable of handling large pose variations
that are relatively underrepresented in training data. This paper presents a
method for learning a feature representation that is invariant to pose, without
requiring extensive pose coverage in training data. We first propose to
generate non-frontal views from a single frontal face, in order to increase the
diversity of training data while preserving accurate facial details that are
critical for identity discrimination. Our next contribution is to seek a rich
embedding that encodes identity features, as well as non-identity ones such as
pose and landmark locations. Finally, we propose a new feature reconstruction
metric learning to explicitly disentangle identity and pose, by demanding
alignment between the feature reconstructions through various combinations of
identity and pose features, which is obtained from two images of the same
subject. Experiments on both controlled and in-the-wild face datasets, such as
MultiPIE, 300WLP and the profile view database CFP, show that our method
consistently outperforms the state-of-the-art, especially on images with large
head pose variations. Detail results and resource are referred to
https://sites.google.com/site/xipengcshomepage/iccv2017

negative:
In recent years, with the advent of deep-learning, face recognition has
achieved exceptional success. However, many of these deep face recognition
models perform much better in handling frontal faces compared to profile faces.
The major reason for poor performance in handling of profile faces is that it
is inherently difficult to learn pose-invariant deep representations that are
useful for profile face recognition. In this paper, we hypothesize that the
profile face domain possesses a latent connection with the frontal face domain
in a latent feature subspace. We look to exploit this latent connection by
projecting the profile faces and frontal faces into a common latent subspace
and perform verification or retrieval in the latent domain. We leverage a
coupled conditional generative adversarial network (cpGAN) structure to find
the hidden relationship between the profile and frontal images in a latent
common embedding subspace. Specifically, the cpGAN framework consists of two
conditional GAN-based sub-networks, one dedicated to the frontal domain and the
other dedicated to the profile domain. Each sub-network tends to find a
projection that maximizes the pair-wise correlation between the two feature
domains in a common embedding feature subspace. The efficacy of our approach
compared with the state-of-the-art is demonstrated using the CFP, CMU
Multi-PIE, IJB-A, and IJB-C datasets. Additionally, we have also implemented a
coupled convolutional neural network (cpCNN) and an adversarial discriminative
domain adaptation network (ADDA) for profile to frontal face recognition. We
have evaluated the performance of cpCNN and ADDA and compared it with the
proposed cpGAN. Finally, we have also evaluated our cpGAN for reconstruction of
frontal faces from input profile faces contained in the VGGFace2 dataset.

negative:
Face Super-Resolution (SR) is a domain-specific super-resolution problem. The
specific facial prior knowledge could be leveraged for better super-resolving
face images. We present a novel deep end-to-end trainable Face Super-Resolution
Network (FSRNet), which makes full use of the geometry prior, i.e., facial
landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR)
face images without well-aligned requirement. Specifically, we first construct
a coarse SR network to recover a coarse high-resolution (HR) image. Then, the
coarse HR image is sent to two branches: a fine SR encoder and a prior
information estimation network, which extracts the image features, and
estimates landmark heatmaps/parsing maps respectively. Both image features and
prior information are sent to a fine SR decoder to recover the HR image. To
further generate realistic faces, we propose the Face Super-Resolution
Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss
into FSRNet. Moreover, we introduce two related tasks, face alignment and
parsing, as the new evaluation metrics for face SR, which address the
inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark
experiments show that FSRNet and FSRGAN significantly outperforms state of the
arts for very LR face SR, both quantitatively and qualitatively. Code will be
made available upon publication.

negative:
Existing face super-resolution (SR) methods mainly assume the input image to
be noise-free. Their performance degrades drastically when applied to
real-world scenarios where the input image is always contaminated by noise. In
this paper, we propose a Facial Attribute Capsules Network (FACN) to deal with
the problem of high-scale super-resolution of noisy face image. Capsule is a
group of neurons whose activity vector models different properties of the same
entity. Inspired by the concept of capsule, we propose an integrated
representation model of facial information, which named Facial Attribute
Capsule (FAC). In the SR processing, we first generated a group of FACs from
the input LR face, and then reconstructed the HR face from this group of FACs.
Aiming to effectively improve the robustness of FAC to noise, we generate FAC
in semantic, probabilistic and facial attributes manners by means of integrated
learning strategy. Each FAC can be divided into two sub-capsules: Semantic
Capsule (SC) and Probabilistic Capsule (PC). Them describe an explicit facial
attribute in detail from two aspects of semantic representation and probability
distribution. The group of FACs model an image as a combination of facial
attribute information in the semantic space and probabilistic space by an
attribute-disentangling way. The diverse FACs could better combine the face
prior information to generate the face images with fine-grained semantic
attributes. Extensive benchmark experiments show that our method achieves
superior hallucination results and outperforms state-of-the-art for very low
resolution (LR) noise face image super resolution.

negative:
In this paper we address the problem of hallucinating high-resolution facial
images from unaligned low-resolution inputs at high magnification factors. We
approach the problem with convolutional neural networks (CNNs) and propose a
novel (deep) face hallucination model that incorporates identity priors into
the learning procedure. The model consists of two main parts: i) a cascaded
super-resolution network that upscales the low-resolution images, and ii) an
ensemble of face recognition models that act as identity priors for the
super-resolution network during training. Different from competing
super-resolution approaches that typically rely on a single model for upscaling
(even with large magnification factors), our network uses a cascade of multiple
SR models that progressively upscale the low-resolution images using steps of
$2\times$. This characteristic allows us to apply supervision signals (target
appearances) at different resolutions and incorporate identity constraints at
multiple-scales. Our model is able to upscale (very) low-resolution images
captured in unconstrained conditions and produce visually convincing results.
We rigorously evaluate the proposed model on a large datasets of facial images
and report superior performance compared to the state-of-the-art.

negative:
Facial image super-resolution (SR) is an important preprocessing for facial
image analysis, face recognition, and image-based 3D face reconstruction.
Recent convolutional neural network (CNN) based method has shown excellent
performance by learning mapping relation using pairs of low-resolution (LR) and
high-resolution (HR) facial images. However, since the HR facial image
reconstruction using CNN is conventionally aimed to increase the PSNR and SSIM
metrics, the reconstructed HR image might not be realistic even with high
scores. An adversarial framework is proposed in this study to reconstruct the
HR facial image by simultaneously generating an HR image with and without blur.
First, the spatial resolution of the LR facial image is increased by eight
times using a five-layer CNN. Then, the encoder extracts the features of the
up-scaled image. These features are finally sent to two branches (decoders) to
generate an HR facial image with and without blur. In addition, local and
global discriminators are combined to focus on the reconstruction of HR facial
structures. Experiment results show that the proposed algorithm generates a
realistic HR facial image. Furthermore, the proposed method can generate a
variety of different facial images.

negative:
Enhancing low resolution images via super-resolution or image synthesis for
cross-resolution face recognition has been well studied. Several image
processing and machine learning paradigms have been explored for addressing the
same. In this research, we propose Synthesis via Deep Sparse Representation
algorithm for synthesizing a high resolution face image from a low resolution
input image. The proposed algorithm learns multi-level sparse representation
for both high and low resolution gallery images, along with an identity aware
dictionary and a transformation function between the two representations for
face identification scenarios. With low resolution test data as input, the high
resolution test image is synthesized using the identity aware dictionary and
transformation which is then used for face recognition. The performance of the
proposed SDSR algorithm is evaluated on four databases, including one real
world dataset. Experimental results and comparison with existing seven
algorithms demonstrate the efficacy of the proposed algorithm in terms of both
face identification and image quality measures.

negative:
We introduce InverseFaceNet, a deep convolutional inverse rendering framework
for faces that jointly estimates facial pose, shape, expression, reflectance
and illumination from a single input image. By estimating all parameters from
just a single image, advanced editing possibilities on a single face image,
such as appearance editing and relighting, become feasible in real time. Most
previous learning-based face reconstruction approaches do not jointly recover
all dimensions, or are severely limited in terms of visual quality. In
contrast, we propose to recover high-quality facial pose, shape, expression,
reflectance and illumination using a deep neural network that is trained using
a large, synthetically created training corpus. Our approach builds on a novel
loss function that measures model-space similarity directly in parameter space
and significantly improves reconstruction accuracy. We further propose a
self-supervised bootstrapping process in the network training loop, which
iteratively updates the synthetic training corpus to better reflect the
distribution of real-world imagery. We demonstrate that this strategy
outperforms completely synthetically trained networks. Finally, we show
high-quality reconstructions and compare our approach to several
state-of-the-art approaches.

negative:
The performance of a convolutional neural network (CNN) based face
recognition model largely relies on the richness of labelled training data.
Collecting a training set with large variations of a face identity under
different poses and illumination changes, however, is very expensive, making
the diversity of within-class face images a critical issue in practice. In this
paper, we propose a 3D model-assisted domain-transferred face augmentation
network (DotFAN) that can generate a series of variants of an input face based
on the knowledge distilled from existing rich face datasets collected from
other domains. DotFAN is structurally a conditional CycleGAN but has two
additional subnetworks, namely face expert network (FEM) and face shape
regressor (FSR), for latent code control. While FSR aims to extract face
attributes, FEM is designed to capture a face identity. With their aid, DotFAN
can learn a disentangled face representation and effectively generate face
images of various facial attributes while preserving the identity of augmented
faces. Experiments show that DotFAN is beneficial for augmenting small face
datasets to improve their within-class diversity so that a better face
recognition model can be learned from the augmented dataset.

negative:
With the powerfulness of convolution neural networks (CNN), CNN based face
reconstruction has recently shown promising performance in reconstructing
detailed face shape from 2D face images. The success of CNN-based methods
relies on a large number of labeled data. The state-of-the-art synthesizes such
data using a coarse morphable face model, which however has difficulty to
generate detailed photo-realistic images of faces (with wrinkles). This paper
presents a novel face data generation method. Specifically, we render a large
number of photo-realistic face images with different attributes based on
inverse rendering. Furthermore, we construct a fine-detailed face image dataset
by transferring different scales of details from one image to another. We also
construct a large number of video-type adjacent frame pairs by simulating the
distribution of real video data. With these nicely constructed datasets, we
propose a coarse-to-fine learning framework consisting of three convolutional
networks. The networks are trained for real-time detailed 3D face
reconstruction from monocular video as well as from a single image. Extensive
experimental results demonstrate that our framework can produce high-quality
reconstruction but with much less computation time compared to the
state-of-the-art. Moreover, our method is robust to pose, expression and
lighting due to the diversity of data.

negative:
Given a really low-resolution input image of a face (say 16x16 or 8x8
pixels), the goal of this paper is to reconstruct a high-resolution version
thereof. This, by itself, is an ill-posed problem, as the high-frequency
information is missing in the low-resolution input and needs to be
hallucinated, based on prior knowledge about the image content. Rather than
relying on a generic face prior, in this paper, we explore the use of a set of
exemplars, i.e. other high-resolution images of the same person. These guide
the neural network as we condition the output on them. Multiple exemplars work
better than a single one. To combine the information from multiple exemplars
effectively, we introduce a pixel-wise weight generation module. Besides
standard face super-resolution, our method allows to perform subtle face
editing simply by replacing the exemplars with another set with different
facial features. A user study is conducted and shows the super-resolved images
can hardly be distinguished from real images on the CelebA dataset. A
qualitative comparison indicates our model outperforms methods proposed in the
literature on the CelebA and WebFace dataset.

negative:
It has been recently shown that neural networks can recover the geometric
structure of a face from a single given image. A common denominator of most
existing face geometry reconstruction methods is the restriction of the
solution space to some low-dimensional subspace. While such a model
significantly simplifies the reconstruction problem, it is inherently limited
in its expressiveness. As an alternative, we propose an Image-to-Image
translation network that jointly maps the input image to a depth image and a
facial correspondence map. This explicit pixel-based mapping can then be
utilized to provide high quality reconstructions of diverse faces under extreme
expressions, using a purely geometric refinement process. In the spirit of
recent approaches, the network is trained only with synthetic data, and is then
evaluated on in-the-wild facial images. Both qualitative and quantitative
analyses demonstrate the accuracy and the robustness of our approach.

negative:
We present a novel deep learning approach to synthesize complete face images
in the presence of large ocular region occlusions. This is motivated by recent
surge of VR/AR displays that hinder face-to-face communications. Different from
the state-of-the-art face inpainting methods that have no control over the
synthesized content and can only handle frontal face pose, our approach can
faithfully recover the missing content under various head poses while
preserving the identity. At the core of our method is a novel generative
network with dedicated constraints to regularize the synthesis process. To
preserve the identity, our network takes an arbitrary occlusion-free image of
the target identity to infer the missing content, and its high-level CNN
features as an identity prior to regularize the searching space of generator.
Since the input reference image may have a different pose, a pose map and a
novel pose discriminator are further adopted to supervise the learning of
implicit pose transformations. Our method is capable of generating coherent
facial inpainting with consistent identity over videos with large variations of
head motions. Experiments on both synthesized and real data demonstrate that
our method greatly outperforms the state-of-the-art methods in terms of both
synthesis quality and robustness.

negative:
Face deblurring aims to restore a clear face image from a blurred input image
with more explicit structure and facial details. However, most conventional
image and face deblurring methods focus on the whole generated image resolution
without consideration of special face part texture and generally produce
unsufficient details. Considering that faces and backgrounds have different
distribution information, in this study, we designed an effective face
deblurring network based on separable normalization and adaptive
denormalization (SNADNet). First, We fine-tuned the face parsing network to
obtain an accurate face structure. Then, we divided the face parsing feature
into face foreground and background. Moreover, we constructed a new feature
adaptive denormalization to regularize fafcial structures as a condition of the
auxiliary to generate more harmonious and undistorted face structure. In
addition, we proposed a texture extractor and multi-patch discriminator to
enhance the generated facial texture information. Experimental results on both
CelebA and CelebA-HQ datasets demonstrate that the proposed face deblurring
network restores face structure with more facial details and performs favorably
against state-of-the-art methods in terms of structured similarity indexing
method (SSIM), peak signal-to-noise ratio (PSNR), Frechet inception distance
(FID) and L1, and qualitative comparisons.

negative:
Recently, it was shown that excellent results can be achieved in both face
landmark localization and pose-invariant face recognition. These breakthroughs
are attributed to the efforts of the community to manually annotate facial
images in many different poses and to collect 3D faces data. In this paper, we
propose a novel method for joint face landmark localization and frontal face
reconstruction (pose correction) using a small set of frontal images only. By
observing that the frontal facial image is the one with the minimum rank from
all different poses we formulate an appropriate model which is able to jointly
recover the facial landmarks as well as the frontalized version of the face. To
this end, a suitable optimization problem, involving the minimization of the
nuclear norm and the matrix $\ell_1$ norm, is solved. The proposed method is
assessed in frontal face reconstruction (pose correction), face landmark
localization, and pose-invariant face recognition and verification by
conducting experiments on $6$ facial images databases. The experimental results
demonstrate the effectiveness of the proposed method.

negative:
Deep convolutional networks (CNNs) have achieved great success in face
completion to generate plausible facial structures. These methods, however, are
limited in maintaining global consistency among face components and recovering
fine facial details. On the other hand, reflectional symmetry is a prominent
property of face image and benefits face recognition and consistency modeling,
yet remaining uninvestigated in deep face completion. In this work, we leverage
two kinds of symmetry-enforcing subnets to form a symmetry-consistent CNN model
(i.e., SymmFCNet) for effective face completion. For missing pixels on only one
of the half-faces, an illumination-reweighted warping subnet is developed to
guide the warping and illumination reweighting of the other half-face. As for
missing pixels on both of half-faces, we present a generative reconstruction
subnet together with a perceptual symmetry loss to enforce symmetry consistency
of recovered structures. The SymmFCNet is constructed by stacking generative
reconstruction subnet upon illumination-reweighted warping subnet, and can be
end-to-end learned from training set of unaligned face images. Experiments show
that SymmFCNet can generate high quality results on images with synthetic and
real occlusion, and performs favorably against state-of-the-arts.

negative:
Although face recognition systems have achieved impressive performance in
recent years, the low-resolution face recognition (LRFR) task remains
challenging, especially when the LR faces are captured under non-ideal
conditions, as is common in surveillance-based applications. Faces captured in
such conditions are often contaminated by blur, nonuniform lighting, and
nonfrontal face pose. In this paper, we analyze face recognition techniques
using data captured under low-quality conditions in the wild. We provide a
comprehensive analysis of experimental results for two of the most important
applications in real surveillance applications, and demonstrate practical
approaches to handle both cases that show promising performance. The following
three contributions are made: {\em (i)} we conduct experiments to evaluate
super-resolution methods for low-resolution face recognition; {\em (ii)} we
study face re-identification on various public face datasets including real
surveillance and low-resolution subsets of large-scale datasets, present a
baseline result for several deep learning based approaches, and improve them by
introducing a GAN pre-training approach and fully convolutional architecture;
and {\em (iii)} we explore low-resolution face identification by employing a
state-of-the-art supervised discriminative learning approach. Evaluations are
conducted on challenging portions of the SCFace and UCCSface datasets.

negative:
Face recognition has been of great importance in many applications as a
biometric for its throughput, convenience, and non-invasiveness. Recent
advancements in deep Convolutional Neural Network (CNN) architectures have
boosted significantly the performance of face recognition based on
two-dimensional (2D) facial texture images and outperformed the previous state
of the art using conventional methods. However, the accuracy of 2D face
recognition is still challenged by the change of pose, illumination, make-up,
and expression. On the other hand, the geometric information contained in
three-dimensional (3D) face data has the potential to overcome the fundamental
limitations of 2D face data.
  We propose a multi-Channel deep 3D face network for face recognition based on
3D face data. We compute the geometric information of a 3D face based on its
piecewise-linear triangular mesh structure and then conformally flatten
geometric information along with the color from 3D to 2D plane to leverage the
state-of-the-art deep CNN architectures. We modify the input layer of the
network to take images with nine channels instead of three only such that more
geometric information can be explicitly fed to it. We pre-train the network
using images from the VGG-Face \cite{Parkhi2015} and then fine-tune it with the
generated multi-channel face images. The face recognition accuracy of the
multi-Channel deep 3D face network has achieved 98.6. The experimental results
also clearly show that the network performs much better when a 9-channel image
is flattened to plane based on the conformal map compared with the orthographic
projection.

negative:
In video based face recognition, face images are typically captured over
multiple frames in uncontrolled conditions, where head pose, illumination,
shadowing, motion blur and focus change over the sequence. Additionally,
inaccuracies in face localisation can also introduce scale and alignment
variations. Using all face images, including images of poor quality, can
actually degrade face recognition performance. While one solution it to use
only the "best" subset of images, current face selection techniques are
incapable of simultaneously handling all of the abovementioned issues. We
propose an efficient patch-based face image quality assessment algorithm which
quantifies the similarity of a face image to a probabilistic face model,
representing an "ideal" face. Image characteristics that affect recognition are
taken into account, including variations in geometric alignment (shift,
rotation and scale), sharpness, head pose and cast shadows. Experiments on
FERET and PIE datasets show that the proposed algorithm is able to identify
images which are simultaneously the most frontal, aligned, sharp and well
illuminated. Further experiments on a new video surveillance dataset (termed
ChokePoint) show that the proposed method provides better face subsets than
existing face selection techniques, leading to significant improvements in
recognition accuracy.

negative:
3D face reconstruction is an important task in the field of computer vision.
Although 3D face reconstruction has being developing rapidly in recent years,
it is still a challenge for face reconstruction under large pose. That is
because much of the information about a face in a large pose will be
unknowable. In order to address this issue, this paper proposes a novel 3D face
reconstruction algorithm (PIFR) based on 3D Morphable Model (3DMM). After input
a single face image, it generates a frontal image by normalizing the image.
Then we set weighted sum of the 3D parameters of the two images. Our method
solves the problem of face reconstruction of a single image of a traditional
method in a large pose, works on arbitrary Pose and Expressions, greatly
improves the accuracy of reconstruction. Experiments on the challenging AFW,
LFPW and AFLW database show that our algorithm significantly improves the
accuracy of 3D face reconstruction even under extreme poses .

negative:
Face images in the wild undergo large intra-personal variations, such as
poses, illuminations, occlusions, and low resolutions, which cause great
challenges to face-related applications. This paper addresses this challenge by
proposing a new deep learning framework that can recover the canonical view of
face images. It dramatically reduces the intra-person variances, while
maintaining the inter-person discriminativeness. Unlike the existing face
reconstruction methods that were either evaluated in controlled 2D environment
or employed 3D information, our approach directly learns the transformation
from the face images with a complex set of variations to their canonical views.
At the training stage, to avoid the costly process of labeling canonical-view
images from the training set by hand, we have devised a new measurement to
automatically select or synthesize a canonical-view image for each identity. As
an application, this face recovery approach is used for face verification.
Facial features are learned from the recovered canonical-view face images by
using a facial component-based convolutional neural network. Our approach
achieves the state-of-the-art performance on the LFW dataset.

negative:
Face recognition approaches often rely on equal image resolution for
verifying faces on two images. However, in practical applications, those image
resolutions are usually not in the same range due to different image capture
mechanisms or sources. In this work, we first analyze the impact of image
resolutions on face verification performance with a state-of-the-art face
recognition model. For images synthetically reduced to $5\,\times\,5$ px
resolution, the verification performance drops from $99.23\%$ increasingly down
to almost $55\%$. Especially for cross-resolution image pairs (one high- and
one low-resolution image), the verification accuracy decreases even further. We
investigate this behavior more in-depth by looking at the feature distances for
every 2-image test pair. To tackle this problem, we propose the following two
methods: 1) Train a state-of-the-art face-recognition model straightforwardly
with $50\%$ low-resolution images directly within each batch. 2) Train a
siamese-network structure and add a cosine distance feature loss between high-
and low-resolution features. Both methods show an improvement for
cross-resolution scenarios and can increase the accuracy at very low resolution
to approximately $70\%$. However, a disadvantage is that a specific model needs
to be trained for every resolution pair. Thus, we extend the aforementioned
methods by training them with multiple image resolutions at once. The
performances for particular testing image resolutions are slightly worse, but
the advantage is that this model can be applied to arbitrary resolution images
and achieves an overall better performance ($97.72\%$ compared to $96.86\%$).
Due to the lack of a benchmark for arbitrary resolution images for the
cross-resolution and equal-resolution task, we propose an evaluation protocol
for five well-known datasets, focusing on high, mid, and low-resolution images.

negative:
Convolutional Neural Networks have reached extremely high performances on the
Face Recognition task. Largely used datasets, such as VGGFace2, focus on
gender, pose and age variations trying to balance them to achieve better
results. However, the fact that images have different resolutions is not
usually discussed and resize to 256 pixels before cropping is used. While
specific datasets for very low resolution faces have been proposed, less
attention has been payed on the task of cross-resolution matching. Such
scenarios are of particular interest for forensic and surveillance systems in
which it usually happens that a low-resolution probe has to be matched with
higher-resolution galleries. While it is always possible to either increase the
resolution of the probe image or to reduce the size of the gallery images, to
the best of our knowledge an extensive experimentation of cross-resolution
matching was missing in the recent deep learning based literature. In the
context of low- and cross-resolution Face Recognition, the contributions of our
work are: i) we proposed a training method to fine-tune a state-of-the-art
model in order to make it able to extract resolution-robust deep features; ii)
we tested our models on the benchmark datasets IJB-B/C considering images at
both full and low resolutions in order to show the effectiveness of the
proposed training algorithm. To the best of our knowledge, this is the first
work testing extensively the performance of a FR model in a cross-resolution
scenario; iii) we tested our models on the low resolution and low quality
datasets QMUL-SurvFace and TinyFace and showed their superior performances,
even though we did not train our model on low-resolution faces only and our
main focus was cross-resolution; iv) we showed that our approach can be more
effective with respect to preprocessing faces with super resolution techniques.

negative:
In this paper, we present an effective and efficient face deblurring
algorithm by exploiting semantic cues via deep convolutional neural networks
(CNNs). As face images are highly structured and share several key semantic
components (e.g., eyes and mouths), the semantic information of a face provides
a strong prior for restoration. As such, we propose to incorporate global
semantic priors as input and impose local structure losses to regularize the
output within a multi-scale deep CNN. We train the network with perceptual and
adversarial losses to generate photo-realistic results and develop an
incremental training strategy to handle random blur kernels in the wild.
Quantitative and qualitative evaluations demonstrate that the proposed face
deblurring algorithm restores sharp images with more facial details and
performs favorably against state-of-the-art methods in terms of restoration
quality, face recognition and execution speed.

negative:
As a domain-specific super-resolution problem, facial image hallucination has
enjoyed a series of breakthroughs thanks to the advances of deep convolutional
neural networks. However, the direct migration of existing methods to video is
still difficult to achieve good performance due to its lack of alignment and
consistency modelling in temporal domain. Taking advantage of high inter-frame
dependency in videos, we propose a self-enhanced convolutional network for
facial video hallucination. It is implemented by making full usage of preceding
super-resolved frames and a temporal window of adjacent low-resolution frames.
Specifically, the algorithm first obtains the initial high-resolution inference
of each frame by taking into consideration a sequence of consecutive
low-resolution inputs through temporal consistency modelling. It further
recurrently exploits the reconstructed results and intermediate features of a
sequence of preceding frames to improve the initial super-resolution of the
current frame by modelling the coherence of structural facial features across
frames. Quantitative and qualitative evaluations demonstrate the superiority of
the proposed algorithm against state-of-the-art methods. Moreover, our
algorithm also achieves excellent performance in the task of general video
super-resolution in a single-shot setting.

negative:
Face super-resolution (FSR), also known as face hallucination, which is aimed
at enhancing the resolution of low-resolution (LR) face images to generate
high-resolution (HR) face images, is a domain-specific image super-resolution
problem. Recently, FSR has received considerable attention and witnessed
dazzling advances with the development of deep learning techniques. To date,
few summaries of the studies on the deep learning-based FSR are available. In
this survey, we present a comprehensive review of deep learning-based FSR
methods in a systematic manner. First, we summarize the problem formulation of
FSR and introduce popular assessment metrics and loss functions. Second, we
elaborate on the facial characteristics and popular datasets used in FSR.
Third, we roughly categorize existing methods according to the utilization of
facial characteristics. In each category, we start with a general description
of design principles, then present an overview of representative approaches,
and then discuss the pros and cons among them. Fourth, we evaluate the
performance of some state-of-the-art methods. Fifth, joint FSR and other tasks,
and FSR-related applications are roughly introduced. Finally, we envision the
prospects of further technological advancement in this field. A curated list of
papers and resources to face super-resolution are available at
\url{https://github.com/junjun-jiang/Face-Hallucination-Benchmark}

negative:
Face analysis techniques have become a crucial component of human-machine
interaction in the fields of assistive and humanoid robotics. However, the
variations in head-pose that arise naturally in these environments are still a
great challenge. In this paper, we present a real-time capable 3D face
modelling framework for 2D in-the-wild images that is applicable for robotics.
The fitting of the 3D Morphable Model is based exclusively on automatically
detected landmarks. After fitting, the face can be corrected in pose and
transformed back to a frontal 2D representation that is more suitable for face
recognition. We conduct face recognition experiments with non-frontal images
from the MUCT database and uncontrolled, in the wild images from the PaSC
database, the most challenging face recognition database to date, showing an
improved performance. Finally, we present our SCITOS G5 robot system, which
incorporates our framework as a means of image pre-processing for face
analysis.

negative:
We propose an Ensemble of Robust Constrained Local Models for alignment of
faces in the presence of significant occlusions and of any unknown pose and
expression. To account for partial occlusions we introduce, Robust Constrained
Local Models, that comprises of a deformable shape and local landmark
appearance model and reasons over binary occlusion labels. Our occlusion
reasoning proceeds by a hypothesize-and-test search over occlusion labels.
Hypotheses are generated by Constrained Local Model based shape fitting over
randomly sampled subsets of landmark detector responses and are evaluated by
the quality of face alignment. To span the entire range of facial pose and
expression variations we adopt an ensemble of independent Robust Constrained
Local Models to search over a discretized representation of pose and
expression. We perform extensive evaluation on a large number of face images,
both occluded and unoccluded. We find that our face alignment system trained
entirely on facial images captured "in-the-lab" exhibits a high degree of
generalization to facial images captured "in-the-wild". Our results are
accurate and stable over a wide spectrum of occlusions, pose and expression
variations resulting in excellent performance on many real-world face datasets.

negative:
Recently, deep learning based 3D face reconstruction methods have shown
promising results in both quality and efficiency.However, training deep neural
networks typically requires a large volume of data, whereas face images with
ground-truth 3D face shapes are scarce. In this paper, we propose a novel deep
3D face reconstruction approach that 1) leverages a robust, hybrid loss
function for weakly-supervised learning which takes into account both low-level
and perception-level information for supervision, and 2) performs multi-image
face reconstruction by exploiting complementary information from different
images for shape aggregation. Our method is fast, accurate, and robust to
occlusion and large pose. We provide comprehensive experiments on three
datasets, systematically comparing our method with fifteen recent methods and
demonstrating its state-of-the-art performance.

negative:
In this paper, we address the problem of face hallucination by proposing a
novel multi-scale generative adversarial network (GAN) architecture optimized
for face verification. First, we propose a multi-scale generator architecture
for face hallucination with a high up-scaling ratio factor, which has multiple
intermediate outputs at different resolutions. The intermediate outputs have
the growing goal of synthesizing small to large images. Second, we incorporate
a face verifier with the original GAN discriminator and propose a novel
discriminator which learns to discriminate different identities while
distinguishing fake generated HR face images from their ground truth images. In
particular, the learned generator cares for not only the visual quality of
hallucinated face images but also preserving the discriminative features in the
hallucination process. In addition, to capture perceptually relevant
differences we employ a perceptual similarity loss, instead of similarity in
pixel space. We perform a quantitative and qualitative evaluation of our
framework on the LFW and CelebA datasets. The experimental results show the
advantages of our proposed method against the state-of-the-art methods on the
8x downsampled testing dataset.

negative:
We address the problem of restoring a high-resolution face image from a
blurry low-resolution input. This problem is difficult as super-resolution and
deblurring need to be tackled simultaneously. Moreover, existing algorithms
cannot handle face images well as low-resolution face images do not have much
texture which is especially critical for deblurring. In this paper, we propose
an effective algorithm by utilizing the domain-specific knowledge of human
faces to recover high-quality faces. We first propose a facial component guided
deep Convolutional Neural Network (CNN) to restore a coarse face image, which
is denoted as the base image where the facial component is automatically
generated from the input face image. However, the CNN based method cannot
handle image details well. We further develop a novel exemplar-based detail
enhancement algorithm via facial component matching. Extensive experiments show
that the proposed method outperforms the state-of-the-art algorithms both
quantitatively and qualitatively.

negative:
Recent advances in image-based 3D human shape estimation have been driven by
the significant improvement in representation power afforded by deep neural
networks. Although current approaches have demonstrated the potential in real
world settings, they still fail to produce reconstructions with the level of
detail often present in the input images. We argue that this limitation stems
primarily form two conflicting requirements; accurate predictions require large
context, but precise predictions require high resolution. Due to memory
limitations in current hardware, previous approaches tend to take low
resolution images as input to cover large spatial context, and produce less
precise (or low resolution) 3D estimates as a result. We address this
limitation by formulating a multi-level architecture that is end-to-end
trainable. A coarse level observes the whole image at lower resolution and
focuses on holistic reasoning. This provides context to an fine level which
estimates highly detailed geometry by observing higher-resolution images. We
demonstrate that our approach significantly outperforms existing
state-of-the-art techniques on single image human shape reconstruction by fully
leveraging 1k-resolution input images.

negative:
Face recognition in real life situations like low illumination condition is
still an open challenge in biometric security. It is well established that the
state-of-the-art methods in face recognition provide low accuracy in the case
of poor illumination. In this work, we propose an algorithm for a more robust
illumination invariant face recognition using a multi-modal approach. We
propose a new dataset consisting of aligned faces of thermal and visual images
of a hundred subjects. We then apply face detection on thermal images using the
biggest blob extraction method and apply them for fusing images of different
modalities for the purpose of face recognition. An algorithm is proposed to
implement fusion of thermal and visual images. We reason for why relying on
only one modality can give erroneous results. We use a lighter and faster CNN
model called MobileNet for the purpose of face recognition with faster
inferencing and to be able to be use it in real time biometric systems. We test
our proposed method on our own created dataset to show that real-time face
recognition on fused images shows far better results than using visual or
thermal images separately.

negative:
Over the last years, with the advent of Generative Adversarial Networks
(GANs), many face analysis tasks have accomplished astounding performance, with
applications including, but not limited to, face generation and 3D face
reconstruction from a single "in-the-wild" image. Nevertheless, to the best of
our knowledge, there is no method which can produce high-resolution
photorealistic 3D faces from "in-the-wild" images and this can be attributed to
the: (a) scarcity of available data for training, and (b) lack of robust
methodologies that can successfully be applied on very high-resolution data. In
this paper, we introduce AvatarMe, the first method that is able to reconstruct
photorealistic 3D faces from a single "in-the-wild" image with an increasing
level of detail. To achieve this, we capture a large dataset of facial shape
and reflectance and build on a state-of-the-art 3D texture and shape
reconstruction method and successively refine its results, while generating the
per-pixel diffuse and specular components that are required for realistic
rendering. As we demonstrate in a series of qualitative and quantitative
experiments, AvatarMe outperforms the existing arts by a significant margin and
reconstructs authentic, 4K by 6K-resolution 3D faces from a single
low-resolution image that, for the first time, bridges the uncanny valley.

negative:
This paper proposes an encoder-decoder network to disentangle shape features
during 3D face reconstruction from single 2D images, such that the tasks of
reconstructing accurate 3D face shapes and learning discriminative shape
features for face recognition can be accomplished simultaneously. Unlike
existing 3D face reconstruction methods, our proposed method directly regresses
dense 3D face shapes from single 2D images, and tackles identity and residual
(i.e., non-identity) components in 3D face shapes explicitly and separately
based on a composite 3D face shape model with latent representations. We devise
a training process for the proposed network with a joint loss measuring both
face identification error and 3D face shape reconstruction error. To construct
training data we develop a method for fitting 3D morphable model (3DMM) to
multiple 2D images of a subject. Comprehensive experiments have been done on
MICC, BU3DFE, LFW and YTF databases. The results show that our method expands
the capacity of 3DMM for capturing discriminative shape features and facial
detail, and thus outperforms existing methods both in 3D face reconstruction
accuracy and in face recognition accuracy.

negative:
We propose a framework based on Generative Adversarial Networks to
disentangle the identity and attributes of faces, such that we can conveniently
recombine different identities and attributes for identity preserving face
synthesis in open domains. Previous identity preserving face synthesis
processes are largely confined to synthesizing faces with known identities that
are already in the training dataset. To synthesize a face with identity outside
the training dataset, our framework requires one input image of that subject to
produce an identity vector, and any other input face image to extract an
attribute vector capturing, e.g., pose, emotion, illumination, and even the
background. We then recombine the identity vector and the attribute vector to
synthesize a new face of the subject with the extracted attribute. Our proposed
framework does not need to annotate the attributes of faces in any way. It is
trained with an asymmetric loss function to better preserve the identity and
stabilize the training process. It can also effectively leverage large amounts
of unlabeled training face images to further improve the fidelity of the
synthesized faces for subjects that are not presented in the labeled training
face dataset. Our experiments demonstrate the efficacy of the proposed
framework. We also present its usage in a much broader set of applications
including face frontalization, face attribute morphing, and face adversarial
example detection.

negative:
Face verification and recognition problems have seen rapid progress in recent
years, however recognition from small size images remains a challenging task
that is inherently intertwined with the task of face super-resolution. Tackling
this problem using multiple frames is an attractive idea, yet requires solving
the alignment problem that is also challenging for low-resolution faces. Here
we present a holistic system for multi-frame recognition, alignment, and
superresolution of faces. Our neural network architecture restores the central
frame of each input sequence additionally taking into account a number of
adjacent frames and making use of sub-pixel movements. We present our results
using the popular dataset for video face recognition (YouTube Faces). We show a
notable improvement of identification score compared to several baselines
including the one based on single-image super-resolution.

negative:
This paper is on image and face super-resolution. The vast majority of prior
work for this problem focus on how to increase the resolution of low-resolution
images which are artificially generated by simple bilinear down-sampling (or in
a few cases by blurring followed by down-sampling).We show that such methods
fail to produce good results when applied to real-world low-resolution, low
quality images. To circumvent this problem, we propose a two-stage process
which firstly trains a High-to-Low Generative Adversarial Network (GAN) to
learn how to degrade and downsample high-resolution images requiring, during
training, only unpaired high and low-resolution images. Once this is achieved,
the output of this network is used to train a Low-to-High GAN for image
super-resolution using this time paired low- and high-resolution images. Our
main result is that this network can be now used to efectively increase the
quality of real-world low-resolution images. We have applied the proposed
pipeline for the problem of face super-resolution where we report large
improvement over baselines and prior work although the proposed method is
potentially applicable to other object categories.

negative:
State-of-the-art deep face recognition approaches report near perfect
performance on popular benchmarks, e.g., Labeled Faces in the Wild. However,
their performance deteriorates significantly when they are applied on low
quality images, such as those acquired by surveillance cameras. A further
challenge for low resolution face recognition for surveillance applications is
the matching of recorded low resolution probe face images with high resolution
reference images, which could be the case in watchlist scenarios. In this
paper, we have addressed these problems and investigated the factors that would
contribute to the identification performance of the state-of-the-art deep face
recognition models when they are applied to low resolution face recognition
under mismatched conditions. We have observed that the following factors affect
performance in a positive way: appearance variety and resolution distribution
of the training dataset, resolution matching between the gallery and probe
images, and the amount of information included in the probe images. By
leveraging this information, we have utilized deep face models trained on
MS-Celeb-1M and fine-tuned on VGGFace2 dataset and achieved state-of-the-art
accuracies on the SCFace and ICB-RW benchmarks, even without using any training
data from the datasets of these benchmarks.

negative:
We propose a novel couple mappings method for low resolution face recognition
using deep convolutional neural networks (DCNNs). The proposed architecture
consists of two branches of DCNNs to map the high and low resolution face
images into a common space with nonlinear transformations. The branch
corresponding to transformation of high resolution images consists of 14 layers
and the other branch which maps the low resolution face images to the common
space includes a 5-layer super-resolution network connected to a 14-layer
network. The distance between the features of corresponding high and low
resolution images are backpropagated to train the networks. Our proposed method
is evaluated on FERET data set and compared with state-of-the-art competing
methods. Our extensive experimental results show that the proposed method
significantly improves the recognition performance especially for very low
resolution probe face images (11.4% improvement in recognition accuracy).
Furthermore, it can reconstruct a high resolution image from its corresponding
low resolution probe image which is comparable with state-of-the-art
super-resolution methods in terms of visual quality.

negative:
In this paper, we present a deep learning based image feature extraction
method designed specifically for face images. To train the feature extraction
model, we construct a large scale photo-realistic face image dataset with
ground-truth correspondence between multi-view face images, which are
synthesized from real photographs via an inverse rendering procedure. The deep
face feature (DFF) is trained using correspondence between face images rendered
from different views. Using the trained DFF model, we can extract a feature
vector for each pixel of a face image, which distinguishes different facial
regions and is shown to be more effective than general-purpose feature
descriptors for face-related tasks such as matching and alignment. Based on the
DFF, we develop a robust face alignment method, which iteratively updates
landmarks, pose and 3D shape. Extensive experiments demonstrate that our method
can achieve state-of-the-art results for face alignment under highly
unconstrained face images.

negative:
Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.

negative:
The area of face recognition is one of the most widely researched areas in
the domain of computer vision and biometric. This is because, the non-intrusive
nature of face biometric makes it comparatively more suitable for application
in area of surveillance at public places such as airports. The application of
primitive methods in face recognition could not give very satisfactory
performance. However, with the advent of machine and deep learning methods and
their application in face recognition, several major breakthroughs were
obtained. The use of 2D Convolution Neural networks(2D CNN) in face recognition
crossed the human face recognition accuracy and reached to 99%. Still, robust
face recognition in the presence of real world conditions such as variation in
resolution, illumination and pose is a major challenge for researchers in face
recognition. In this work, we used video as input to the 3D CNN architectures
for capturing both spatial and time domain information from the video for face
recognition in real world environment. For the purpose of experimentation, we
have developed our own video dataset called CVBL video dataset. The use of 3D
CNN for face recognition in videos shows promising results with DenseNets
performing the best with an accuracy of 97% on CVBL dataset.

negative:
Majority of the face recognition algorithms use query faces captured from
uncontrolled, in the wild, environment. Often caused by the cameras limited
capabilities, it is common for these captured facial images to be blurred or
low resolution. Super resolution algorithms are therefore crucial in improving
the resolution of such images especially when the image size is small requiring
enlargement. This paper aims to demonstrate the effect of one of the
state-of-the-art algorithms in the field of image super resolution. To
demonstrate the functionality of the algorithm, various before and after 3D
face alignment cases are provided using the images from the Labeled Faces in
the Wild (lfw). Resulting images are subject to testing on a closed set face
recognition protocol using unsupervised algorithms with high dimension
extracted features. The inclusion of super resolution algorithm resulted in
significant improved recognition rate over recently reported results obtained
from unsupervised algorithms.

negative:
With the recent success of deep neural networks, remarkable progress has been
achieved on face recognition. However, collecting large-scale real-world
training data for face recognition has turned out to be challenging, especially
due to the label noise and privacy issues. Meanwhile, existing face recognition
datasets are usually collected from web images, lacking detailed annotations on
attributes (e.g., pose and expression), so the influences of different
attributes on face recognition have been poorly investigated. In this paper, we
address the above-mentioned issues in face recognition using synthetic face
images, i.e., SynFace. Specifically, we first explore the performance gap
between recent state-of-the-art face recognition models trained with synthetic
and real face images. We then analyze the underlying causes behind the
performance gap, e.g., the poor intra-class variations and the domain gap
between synthetic and real face images. Inspired by this, we devise the SynFace
with identity mixup (IM) and domain mixup (DM) to mitigate the above
performance gap, demonstrating the great potentials of synthetic data for face
recognition. Furthermore, with the controllable face synthesis model, we can
easily manage different factors of synthetic face generation, including pose,
expression, illumination, the number of identities, and samples per identity.
Therefore, we also perform a systematically empirical analysis on synthetic
face images to provide some insights on how to effectively utilize synthetic
data for face recognition.

negative:
The objective of this paper is a neural network model that controls the pose
and expression of a given face, using another face or modality (e.g. audio).
This model can then be used for lightweight, sophisticated video and image
editing.
  We make the following three contributions. First, we introduce a network,
X2Face, that can control a source face (specified by one or more frames) using
another face in a driving frame to produce a generated frame with the identity
of the source frame but the pose and expression of the face in the driving
frame. Second, we propose a method for training the network fully
self-supervised using a large collection of video data. Third, we show that the
generation process can be driven by other modalities, such as audio or pose
codes, without any further training of the network.
  The generation results for driving a face with another face are compared to
state-of-the-art self-supervised/supervised methods. We show that our approach
is more robust than other methods, as it makes fewer assumptions about the
input data. We also show examples of using our framework for video face
editing.

negative:
As the deep learning makes big progresses in still-image face recognition,
unconstrained video face recognition is still a challenging task due to low
quality face images caused by pose, blur, occlusion, illumination etc. In this
paper we propose a network for face recognition which gives an explicit and
quantitative quality score at the same time when a feature vector is extracted.
To our knowledge this is the first network that implements these two functions
in one network online. This network is very simple by adding a quality network
branch to the baseline network of face recognition. It does not require
training datasets with annotated face quality labels. We evaluate this network
on both still-image face datasets and video face datasets and achieve the
state-of-the-art performance in many cases. This network enables a lot of
applications where an explicit face quality scpre is used. We demonstrate three
applications of the explicit face quality, one of which is a progressive
feature aggregation scheme in online video face recognition. We design an
experiment to prove the benefits of using the face quality in this application.
Code will be available at \url{https://github.com/deepcam-cn/facequality}.

negative:
Nowadays, due to the ubiquitous visual media there are vast amounts of
already available high-resolution (HR) face images. Therefore, for
super-resolving a given very low-resolution (LR) face image of a person it is
very likely to find another HR face image of the same person which can be used
to guide the process. In this paper, we propose a convolutional neural network
(CNN)-based solution, namely GWAInet, which applies super-resolution (SR) by a
factor 8x on face images guided by another unconstrained HR face image of the
same person with possible differences in age, expression, pose or size. GWAInet
is trained in an adversarial generative manner to produce the desired high
quality perceptual image results. The utilization of the HR guiding image is
realized via the use of a warper subnetwork that aligns its contents to the
input image and the use of a feature fusion chain for the extracted features
from the warped guiding image and the input image. In training, the identity
loss further helps in preserving the identity related features by minimizing
the distance between the embedding vectors of SR and HR ground truth images.
Contrary to the current state-of-the-art in face super-resolution, our method
does not require facial landmark points for its training, which helps its
robustness and allows it to produce fine details also for the surrounding face
region in a uniform manner. Our method GWAInet produces photo-realistic images
in upscaling factor 8x and outperforms state-of-the-art in quantitative terms
and perceptual quality.

negative:
Landmark detection algorithms trained on high resolution images perform
poorly on datasets containing low resolution images. This deters the
performance of algorithms relying on quality landmarks, for example, face
recognition. To the best of our knowledge, there does not exist any dataset
consisting of low resolution face images along with their annotated landmarks,
making supervised training infeasible. In this paper, we present a
semi-supervised approach to predict landmarks on low resolution images by
learning them from labeled high resolution images. The objective of this work
is to show that predicting landmarks directly on low resolution images is more
effective than the current practice of aligning images after rescaling or
superresolution. In a two-step process, the proposed approach first learns to
generate low resolution images by modeling the distribution of target low
resolution images. In the second stage, the roles of generated images and real
low resolution images are switched and the model learns to predict landmarks
for real low resolution images from generated low resolution images. With
extensive experimentation, we study the impact of each of the design choices
and also show that prediction of landmarks directly on low resolution images
improves the performance of important tasks such as face recognition in low
resolution images.

negative:
Face liveness detection is an essential prerequisite for face recognition
applications. Previous face liveness detection methods usually train a binary
classifier to differentiate between a fake face and a real face before face
recognition. The client identity information is not utilized in previous face
liveness detection methods. However, in practical face recognition
applications, face spoofing attacks are always aimed at a specific client, and
the client identity information can provide useful clues for face liveness
detection. In this paper, we propose a face liveness detection method based on
the client identity using Siamese network. We detect face liveness after face
recognition instead of before face recognition, that is, we detect face
liveness with the client identity information. We train a Siamese network with
image pairs. Each image pair consists of two real face images or one real and
one fake face images. The face images in each pair come from a same client.
Given a test face image, the face image is firstly recognized by face
recognition system, then the real face image of the identified client is
retrieved to help the face liveness detection. Experiment results demonstrate
the effectiveness of our method.

negative:
With the growing importance of preventing the COVID-19 virus, face images
obtained in most video surveillance scenarios are low resolution with mask
simultaneously. However, most of the previous face super-resolution solutions
can not handle both tasks in one model. In this work, we treat the mask
occlusion as image noise and construct a joint and collaborative learning
network, called JDSR-GAN, for the masked face super-resolution task. Given a
low-quality face image with the mask as input, the role of the generator
composed of a denoising module and super-resolution module is to acquire a
high-quality high-resolution face image. The discriminator utilizes some
carefully designed loss functions to ensure the quality of the recovered face
images. Moreover, we incorporate the identity information and attention
mechanism into our network for feasible correlated feature expression and
informative feature learning. By jointly performing denoising and face
super-resolution, the two tasks can complement each other and attain promising
performance. Extensive qualitative and quantitative results show the
superiority of our proposed JDSR-GAN over some comparable methods which perform
the previous two tasks separately.

negative:
Face quality assessment aims at estimating the utility of a face image for
the purpose of recognition. It is a key factor to achieve high face recognition
performances. Currently, the high performance of these face recognition systems
come with the cost of a strong bias against demographic and non-demographic
sub-groups. Recent work has shown that face quality assessment algorithms
should adapt to the deployed face recognition system, in order to achieve
highly accurate and robust quality estimations. However, this could lead to a
bias transfer towards the face quality assessment leading to discriminatory
effects e.g. during enrolment. In this work, we present an in-depth analysis of
the correlation between bias in face recognition and face quality assessment.
Experiments were conducted on two publicly available datasets captured under
controlled and uncontrolled circumstances with two popular face embeddings. We
evaluated four state-of-the-art solutions for face quality assessment towards
biases to pose, ethnicity, and age. The experiments showed that the face
quality assessment solutions assign significantly lower quality values towards
subgroups affected by the recognition bias demonstrating that these approaches
are biased as well. This raises ethical questions towards fairness and
discrimination which future works have to address.

negative:
Learned 3D representations of human faces are useful for computer vision
problems such as 3D face tracking and reconstruction from images, as well as
graphics applications such as character generation and animation. Traditional
models learn a latent representation of a face using linear subspaces or
higher-order tensor generalizations. Due to this linearity, they can not
capture extreme deformations and non-linear expressions. To address this, we
introduce a versatile model that learns a non-linear representation of a face
using spectral convolutions on a mesh surface. We introduce mesh sampling
operations that enable a hierarchical mesh representation that captures
non-linear variations in shape and expression at multiple scales within the
model. In a variational setting, our model samples diverse realistic 3D faces
from a multivariate Gaussian distribution. Our training data consists of 20,466
meshes of extreme expressions captured over 12 different subjects. Despite
limited training data, our trained model outperforms state-of-the-art face
models with 50% lower reconstruction error, while using 75% fewer parameters.
We also show that, replacing the expression space of an existing
state-of-the-art face model with our autoencoder, achieves a lower
reconstruction error. Our data, model and code are available at
http://github.com/anuragranj/coma

negative:
Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.

negative:
Recent progress in deep learning-based models has improved photo-realistic
(or perceptual) single-image super-resolution significantly. However, despite
their powerful performance, many methods are difficult to apply to real-world
applications because of the heavy computational requirements. To facilitate the
use of a deep model under such demands, we focus on keeping the network
efficient while maintaining its performance. In detail, we design an
architecture that implements a cascading mechanism on a residual network to
boost the performance with limited resources via multi-level feature fusion. In
addition, our proposed model adopts group convolution and recursive schemes in
order to achieve extreme efficiency. We further improve the perceptual quality
of the output by employing the adversarial learning paradigm and a multi-scale
discriminator approach. The performance of our method is investigated through
extensive internal experiments and benchmarks using various datasets. Our
results show that our models outperform the recent methods with similar
complexity, for both traditional pixel-based and perception-based tasks.

negative:
Deep Convolutional Neural Networks (CNNs) have been one of the most
influential recent developments in computer vision, particularly for
categorization. There is an increasing demand for explainable AI as these
systems are deployed in the real world. However, understanding the information
represented and processed in CNNs remains in most cases challenging. Within
this paper, we explore the use of new information theoretic techniques
developed in the field of neuroscience to enable novel understanding of how a
CNN represents information. We trained a 10-layer ResNet architecture to
identify 2,000 face identities from 26M images generated using a rigorously
controlled 3D face rendering model that produced variations of intrinsic (i.e.
face morphology, gender, age, expression and ethnicity) and extrinsic factors
(i.e. 3D pose, illumination, scale and 2D translation). With our methodology,
we demonstrate that unlike human's network overgeneralizes face identities even
with extreme changes of face shape, but it is more sensitive to changes of
texture. To understand the processing of information underlying these
counterintuitive properties, we visualize the features of shape and texture
that the network processes to identify faces. Then, we shed a light into the
inner workings of the black box and reveal how hidden layers represent these
features and whether the representations are invariant to pose. We hope that
our methodology will provide an additional valuable tool for interpretability
of CNNs.

negative:
We present a versatile model, FaceAnime, for various video generation tasks
from still images. Video generation from a single face image is an interesting
problem and usually tackled by utilizing Generative Adversarial Networks (GANs)
to integrate information from the input face image and a sequence of sparse
facial landmarks. However, the generated face images usually suffer from
quality loss, image distortion, identity change, and expression mismatching due
to the weak representation capacity of the facial landmarks. In this paper, we
propose to "imagine" a face video from a single face image according to the
reconstructed 3D face dynamics, aiming to generate a realistic and
identity-preserving face video, with precisely predicted pose and facial
expression. The 3D dynamics reveal changes of the facial expression and motion,
and can serve as a strong prior knowledge for guiding highly realistic face
video generation. In particular, we explore face video prediction and exploit a
well-designed 3D dynamic prediction network to predict a 3D dynamic sequence
for a single face image. The 3D dynamics are then further rendered by the
sparse texture mapping algorithm to recover structural details and sparse
textures for generating face frames. Our model is versatile for various AR/VR
and entertainment applications, such as face video retargeting and face video
prediction. Superior experimental results have well demonstrated its
effectiveness in generating high-fidelity, identity-preserving, and visually
pleasant face video clips from a single source face image.

negative:
Face detection and tracking in low resolution image is not a trivial task due
to the limitation in the appearance features for face characterization.
Moreover, facial expression gives additional distortion on this small and noisy
face. In this paper, we propose deformable symmetric Gabor wavelet network face
model for face detection in low resolution image. Our model optimizes the
rotation, translation, dilation, perspective and partial deformation amount of
the face model with symmetry constraints. Symmetry constraints help our model
to be more robust to noise and distortion. Experimental results on our low
resolution face image dataset and videos show promising face detection and
tracking results under various challenging conditions.

negative:
Face recognition (FR) is one of the most extensively investigated problems in
computer vision. Significant progress in FR has been made due to the recent
introduction of the larger scale FR challenges, particularly with constrained
social media web images, e.g. high-resolution photos of celebrity faces taken
by professional photo-journalists. However, the more challenging FR in
unconstrained and low-resolution surveillance images remains largely
under-studied. To facilitate more studies on developing FR models that are
effective and robust for low-resolution surveillance facial images, we
introduce a new Surveillance Face Recognition Challenge, which we call the
QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly
the only true surveillance FR benchmark to our best knowledge, where
low-resolution images are not synthesised by artificial down-sampling of native
high-resolution images. This challenge contains 463,507 face images of 15,573
distinct identities captured in real-world uncooperative surveillance scenes
over wide space and time. As a consequence, it presents an extremely
challenging FR benchmark. We benchmark the FR performance on this challenge
using five representative deep learning face recognition models, in comparison
to existing benchmarks. We show that the current state of the arts are still
far from being satisfactory to tackle the under-investigated surveillance FR
problem in practical forensic scenarios. Face recognition is generally more
difficult in an open-set setting which is typical for surveillance scenarios,
owing to a large number of non-target people (distractors) appearing open
spaced scenes. This is evidently so that on the new Surveillance FR Challenge,
the top-performing CentreFace deep learning FR model on the MegaFace benchmark
can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.

negative:
Most of the face hallucination methods are designed for complete inputs. They
will not work well if the inputs are very tiny or contaminated by large
occlusion. Inspired by this fact, we propose an obscured face hallucination
network(OFHNet). The OFHNet consists of four parts: an inpainting network, an
upsampling network, a discriminative network, and a fixed facial landmark
detection network. The inpainting network restores the low-resolution(LR)
obscured face images. The following upsampling network is to upsample the
output of inpainting network. In order to ensure the generated
high-resolution(HR) face images more photo-realistic, we utilize the
discriminative network and the facial landmark detection network to better the
result of upsampling network. In addition, we present a semantic structure
loss, which makes the generated HR face images more pleasing. Extensive
experiments show that our framework can restore the appealing HR face images
from 1/4 missing area LR face images with a challenging scaling factor of 8x.

negative:
Visual kinship verification entails confirming whether or not two individuals
in a given pair of images or videos share a hypothesized kin relation. As a
generalized face verification task, visual kinship verification is particularly
difficult with low-quality found Internet data. Due to uncontrolled variations
in background, pose, facial expression, blur, illumination and occlusion,
state-of-the-art methods fail to provide high level of recognition accuracy. As
with many other visual recognition tasks, kinship verification may benefit from
combining visual and audio signals. However, voice-based kinship verification
has received very little prior attention. We hypothesize that the human voice
contains kin-related cues that are complementary to visual cues. In this paper,
we address, for the first time, the use of audio-visual information from face
and voice modalities to perform kinship verification. We first propose a new
multi-modal kinship dataset, called TALking KINship (TALKIN), that contains
several pairs of Internet-quality video sequences. Using TALKIN, we then study
the utility of various kinship verification methods including traditional local
feature based methods, statistical methods and more recent deep learning
approaches. Then, early and late fusion methods are evaluated on the TALKIN
dataset for the study of kinship verification with both face and voice
modalities. Finally, we propose a deep Siamese fusion network with contrastive
loss for multi-modal fusion of kinship relations. Extensive experiments on the
TALKIN dataset indicate that by combining face and voice modalities, the
proposed Siamese network can provide a significantly higher level of accuracy
compared to baseline uni-modal and multi-modal fusion techniques. Experimental
results also indicate that audio (vocal) information is complementary (to
facial information) and useful for kinship verification.

negative:
We propose using the Wasserstein loss for training in inverse problems. In
particular, we consider a learned primal-dual reconstruction scheme for
ill-posed inverse problems using the Wasserstein distance as loss function in
the learning. This is motivated by miss-alignments in training data, which when
using standard mean squared error loss could severely degrade reconstruction
quality. We prove that training with the Wasserstein loss gives a
reconstruction operator that correctly compensates for miss-alignments in
certain cases, whereas training with the mean squared error gives a smeared
reconstruction. Moreover, we demonstrate these effects by training a
reconstruction algorithm using both mean squared error and optimal transport
loss for a problem in computerized tomography.

negative:
Face recognition has been widely studied due to its importance in different
applications; however, most of the proposed methods fail when face images are
occluded or captured under illumination and pose variations. Recently several
low-rank dictionary learning methods have been proposed and achieved promising
results for noisy observations. While these methods are mostly developed for
single-modality scenarios, recent studies demonstrated the advantages of
feature fusion from multiple inputs. We propose a multi-modal structured
low-rank dictionary learning method for robust face recognition, using raw
pixels of face images and their illumination invariant representation. The
proposed method learns robust and discriminative representations from
contaminated face images, even if there are few training samples with large
intra-class variations. Extensive experiments on different datasets validate
the superior performance and robustness of our method to severe illumination
variations and occlusion.

negative:
3D face reconstruction is a fundamental task that can facilitate numerous
applications such as robust facial analysis and augmented reality. It is also a
challenging task due to the lack of high-quality datasets that can fuel current
deep learning-based methods. However, existing datasets are limited in
quantity, realisticity and diversity. To circumvent these hurdles, we introduce
Pixel-Face, a large-scale, high-resolution and diverse 3D face dataset with
massive annotations. Specifically, Pixel-Face contains 855 subjects aging from
18 to 80. Each subject has more than 20 samples with various expressions. Each
sample is composed of high-resolution multi-view RGB images and 3D meshes with
various expressions. Moreover, we collect precise landmarks annotation and 3D
registration result for each data. To demonstrate the advantages of Pixel-Face,
we re-parameterize the 3D Morphable Model (3DMM) into Pixel-3DM using the
collected data. We show that the obtained Pixel-3DM is better in modeling a
wide range of face shapes and expressions. We also carefully benchmark existing
3D face reconstruction methods on our dataset. Moreover, Pixel-Face serves as
an effective training source. We observe that the performance of current face
reconstruction models significantly improves both on existing benchmarks and
Pixel-Face after being fine-tuned using our newly collected data. Extensive
experiments demonstrate the effectiveness of Pixel-3DM and the usefulness of
Pixel-Face.

negative:
3D human shape and pose estimation from monocular images has been an active
area of research in computer vision, having a substantial impact on the
development of new applications, from activity recognition to creating virtual
avatars. Existing deep learning methods for 3D human shape and pose estimation
rely on relatively high-resolution input images; however, high-resolution
visual content is not always available in several practical scenarios such as
video surveillance and sports broadcasting. Low-resolution images in real
scenarios can vary in a wide range of sizes, and a model trained in one
resolution does not typically degrade gracefully across resolutions. Two common
approaches to solve the problem of low-resolution input are applying
super-resolution techniques to the input images which may result in visual
artifacts, or simply training one model for each resolution, which is
impractical in many realistic applications. To address the above issues, this
paper proposes a novel algorithm called RSC-Net, which consists of a
Resolution-aware network, a Self-supervision loss, and a Contrastive learning
scheme. The proposed network is able to learn the 3D body shape and pose across
different resolutions with a single model. The self-supervision loss encourages
scale-consistency of the output, and the contrastive learning scheme enforces
scale-consistency of the deep features. We show that both these new training
losses provide robustness when learning 3D shape and pose in a
weakly-supervised manner. Extensive experiments demonstrate that the RSC-Net
can achieve consistently better results than the state-of-the-art methods for
challenging low-resolution images.

negative:
We present a simple method to reconstruct a high-resolution video from a
face-video, where the identity of a person is obscured by pixelization. This
concealment method is popular because the viewer can still perceive a human
face figure and the overall head motion. However, we show in our experiments
that a fairly good approximation of the original video can be reconstructed in
a way that compromises anonymity. Our system exploits the simultaneous
similarity and small disparity between close-by video frames depicting a human
face, and employs a spatial transformation component that learns the alignment
between the pixelated frames. Each frame, supported by its aligned surrounding
frames, is first encoded, then decoded to a higher resolution. Reconstruction
and perceptual losses promote adherence to the ground-truth, and an adversarial
loss assists in maintaining domain faithfulness. There is no need for explicit
temporal coherency loss as it is maintained implicitly by the alignment of
neighboring frames and reconstruction. Although simple, our framework
synthesizes high-quality face reconstructions, demonstrating that given the
statistical prior of a human face, multiple aligned pixelated frames contain
sufficient information to reconstruct a high-quality approximation of the
original signal.

negative:
Face detection is to search all the possible regions for faces in images and
locate the faces if there are any. Many applications including face
recognition, facial expression recognition, face tracking and head-pose
estimation assume that both the location and the size of faces are known in the
image. In recent decades, researchers have created many typical and efficient
face detectors from the Viola-Jones face detector to current CNN-based ones.
However, with the tremendous increase in images and videos with variations in
face scale, appearance, expression, occlusion and pose, traditional face
detectors are challenged to detect various "in the wild" faces. The emergence
of deep learning techniques brought remarkable breakthroughs to face detection
along with the price of a considerable increase in computation. This paper
introduces representative deep learning-based methods and presents a deep and
thorough analysis in terms of accuracy and efficiency. We further compare and
discuss the popular and challenging datasets and their evaluation metrics. A
comprehensive comparison of several successful deep learning-based face
detectors is conducted to uncover their efficiency using two metrics: FLOPs and
latency. The paper can guide to choose appropriate face detectors for different
applications and also to develop more efficient and accurate detectors.

negative:
Face detection is a well-explored problem. Many challenges on face detectors
like extreme pose, illumination, low resolution and small scales are studied in
the previous work. However, previous proposed models are mostly trained and
tested on good-quality images which are not always the case for practical
applications like surveillance systems. In this paper, we first review the
current state-of-the-art face detectors and their performance on benchmark
dataset FDDB, and compare the design protocols of the algorithms. Secondly, we
investigate their performance degradation while testing on low-quality images
with different levels of blur, noise, and contrast. Our results demonstrate
that both hand-crafted and deep-learning based face detectors are not robust
enough for low-quality images. It inspires researchers to produce more robust
design for face detection in the wild.

negative:
3D human pose and shape estimation from monocular images has been an active
research area in computer vision. Existing deep learning methods for this task
rely on high-resolution input, which however, is not always available in many
scenarios such as video surveillance and sports broadcasting. Two common
approaches to deal with low-resolution images are applying super-resolution
techniques to the input, which may result in unpleasant artifacts, or simply
training one model for each resolution, which is impractical in many realistic
applications.
  To address the above issues, this paper proposes a novel algorithm called
RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss,
and a Contrastive learning scheme. The proposed method is able to learn 3D body
pose and shape across different resolutions with one single model. The
self-supervision loss enforces scale-consistency of the output, and the
contrastive learning scheme enforces scale-consistency of the deep features. We
show that both these new losses provide robustness when learning in a
weakly-supervised manner. Moreover, we extend the RSC-Net to handle
low-resolution videos and apply it to reconstruct textured 3D pedestrians from
low-resolution input. Extensive experiments demonstrate that the RSC-Net can
achieve consistently better results than the state-of-the-art methods for
challenging low-resolution images.

negative:
Although deep learning approaches have achieved performance surpassing humans
for still image-based face recognition, unconstrained video-based face
recognition is still a challenging task due to large volume of data to be
processed and intra/inter-video variations on pose, illumination, occlusion,
scene, blur, video quality, etc. In this work, we consider challenging
scenarios for unconstrained video-based face recognition from multiple-shot
videos and surveillance videos with low-quality frames. To handle these
problems, we propose a robust and efficient system for unconstrained
video-based face recognition, which is composed of modules for face/fiducial
detection, face association, and face recognition. First, we use multi-scale
single-shot face detectors to efficiently localize faces in videos. The
detected faces are then grouped respectively through carefully designed face
association methods, especially for multi-shot videos. Finally, the faces are
recognized by the proposed face matcher based on an unsupervised subspace
learning approach and a subspace-to-subspace similarity metric. Extensive
experiments on challenging video datasets, such as Multiple Biometric Grand
Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus
Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and
IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the
proposed system can accurately detect and associate faces from unconstrained
videos and effectively learn robust and discriminative features for
recognition.

negative:
Face recognition technology has been widely adopted in many mission-critical
scenarios like means of human identification, controlled admission, and mobile
device access, etc. Security surveillance is a typical scenario of face
recognition technology. Because the low-resolution feature of surveillance
video and images makes it difficult for high-resolution face recognition
algorithms to extract effective feature information, Algorithms applied to
high-resolution face recognition are difficult to migrate directly to
low-resolution situations. As face recognition in security surveillance becomes
more important in the era of dense urbanization, it is essential to develop
algorithms that are able to provide satisfactory performance in processing the
video frames generated by low-resolution surveillance cameras. This paper study
on the Correlation Features-based Face Recognition (CoFFaR) method which using
for homogeneous low-resolution surveillance videos, the theory, experimental
details, and experimental results are elaborated in detail. The experimental
results validate the effectiveness of the correlation features method that
improves the accuracy of homogeneous face recognition in surveillance security
scenarios.

negative:
3D face reconstruction is a challenging problem but also an important task in
the field of computer vision and graphics. Recently, many researchers put
attention to the problem and a large number of articles have been published.
Single image reconstruction is one of the branches of 3D face reconstruction,
which has a lot of applications in our life. This paper is a review of the
recent literature on 3D face reconstruction from a single image.

negative:
While existing face recognition systems based on local features are robust to
issues such as misalignment, they can exhibit accuracy degradation when
comparing images of differing resolutions. This is common in surveillance
environments where a gallery of high resolution mugshots is compared to low
resolution CCTV probe images, or where the size of a given image is not a
reliable indicator of the underlying resolution (eg. poor optics). To alleviate
this degradation, we propose a compensation framework which dynamically chooses
the most appropriate face recognition system for a given pair of image
resolutions. This framework applies a novel resolution detection method which
does not rely on the size of the input images, but instead exploits the
sensitivity of local features to resolution using a probabilistic multi-region
histogram approach. Experiments on a resolution-modified version of the
"Labeled Faces in the Wild" dataset show that the proposed resolution detector
frontend obtains a 99% average accuracy in selecting the most appropriate face
recognition system, resulting in higher overall face discrimination accuracy
(across several resolutions) compared to the individual baseline face
recognition systems.

negative:
This paper presents an easy and efficient face detection and face recognition
approach using free software components from the internet. Face detection and
face recognition problems have wide applications in home and office security.
Therefore this work will helpful for those searching for a free face
off-the-shelf face detection system. Using this system, faces can be detected
in uncontrolled environments. In the detection phase, every individual face is
detected and in the recognition phase the detected faces are compared with the
faces in a given data set and recognized.

negative:
The widespread deployment of surveillance cameras for facial recognition
gives rise to many privacy concerns. This study proposes a privacy-friendly
alternative to large scale facial recognition. While there are multiple
techniques to preserve privacy, our work is based on the minimization principle
which implies minimizing the amount of collected personal data. Instead of
running facial recognition software on all video data, we propose to
automatically extract a high quality snapshot of each detected person without
revealing his or her identity. This snapshot is then encrypted and access is
only granted after legal authorization. We introduce a novel unsupervised face
image quality assessment method which is used to select the high quality
snapshots. For this, we train a variational autoencoder on high quality face
images from a publicly available dataset and use the reconstruction probability
as a metric to estimate the quality of each face crop. We experimentally
confirm that the reconstruction probability can be used as biometric quality
predictor. Unlike most previous studies, we do not rely on a manually defined
face quality metric as everything is learned from data. Our face quality
assessment method outperforms supervised, unsupervised and general image
quality assessment methods on the task of improving face verification
performance by rejecting low quality images. The effectiveness of the whole
system is validated qualitatively on still images and videos.

negative:
Video super-resolution aims at generating a high-resolution video from its
low-resolution counterpart. With the rapid rise of deep learning, many recently
proposed video super-resolution methods use convolutional neural networks in
conjunction with explicit motion compensation to capitalize on statistical
dependencies within and across low-resolution frames. Two common issues of such
methods are noteworthy. Firstly, the quality of the final reconstructed HR
video is often very sensitive to the accuracy of motion estimation. Secondly,
the warp grid needed for motion compensation, which is specified by the two
flow maps delineating pixel displacements in horizontal and vertical
directions, tends to introduce additional errors and jeopardize the temporal
consistency across video frames. To address these issues, we propose a novel
dynamic local filter network to perform implicit motion estimation and
compensation by employing, via locally connected layers, sample-specific and
position-specific dynamic local filters that are tailored to the target pixels.
We also propose a global refinement network based on ResBlock and autoencoder
structures to exploit non-local correlations and enhance the spatial
consistency of super-resolved frames. The experimental results demonstrate that
the proposed method outperforms the state-of-the-art, and validate its strength
in terms of local transformation handling, temporal consistency as well as edge
sharpness.

negative:
Cameras mounted on Micro Aerial Vehicles (MAVs) are increasingly used for
recreational photography. However, aerial photographs of public places often
contain faces of bystanders thus leading to a perceived or actual violation of
privacy. To address this issue, we propose to pseudo-randomly modify the
appearance of face regions in the images using a privacy filter that prevents a
human or a face recogniser from inferring the identities of people. The filter,
which is applied only when the resolution is high enough for a face to be
recognisable, adaptively distorts the face appearance as a function of its
resolution. Moreover, the proposed filter locally changes its parameters to
discourage attacks that use parameter estimation. The filter exploits both
global adaptiveness to reduce distortion and local hopping of the parameters to
make their estimation difficult for an attacker. In order to evaluate the
efficiency of the proposed approach, we use a state-of-the-art face recognition
algorithm and synthetically generated face data with 3D geometric image
transformations that mimic faces captured from an MAV at different heights and
pitch angles. Experimental results show that the proposed filter protects
privacy while reducing distortion and exhibits resilience against attacks.