  Plenty of effective methods have been proposed for face recognition during
the past decade. Although these methods differ essentially in many aspects, a
common practice of them is to specifically align the facial area based on the
prior knowledge of human face structure before feature extraction. In most
systems, the face alignment module is implemented independently. This has
actually caused difficulties in the designing and training of end-to-end face
recognition models. In this paper we study the possibility of alignment
learning in end-to-end face recognition, in which neither prior knowledge on
facial landmarks nor artificially defined geometric transformations are
required. Specifically, spatial transformer layers are inserted in front of the
feature extraction layers in a Convolutional Neural Network (CNN) for face
recognition. Only human identity clues are used for driving the neural network
to automatically learn the most suitable geometric transformation and the most
appropriate facial area for the recognition task. To ensure reproducibility,
our model is trained purely on the publicly available CASIA-WebFace dataset,
and is tested on the Labeled Face in the Wild (LFW) dataset. We have achieved a
verification accuracy of 99.08\% which is comparable to state-of-the-art single
model based methods.

Computer Vision and Pattern Recognition