  Human head pose estimation in images has applications in many fields such as
human-computer interaction or video surveillance tasks. In this work, we
address this problem, defined here as the estimation of both vertical
(tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single
Convolutional Neural Network (ConvNet) model, trying to balance precision and
inference speed in order to maximize its usability in real-world applications.
Our model is trained over the combination of two datasets: 'Pointing'04'
(aiming at covering a wide range of poses) and 'Annotated Facial Landmarks in
the Wild' (in order to improve robustness of our model for its use on
real-world images). Three different partitions of the combined dataset are
defined and used for training, validation and testing purposes. As a result of
this work, we have obtained a trained ConvNet model, coined RealHePoNet, that
given a low-resolution grayscale input image, and without the need of using
facial landmarks, is able to estimate with low error both tilt and pan angles
(~4.4{\deg} average error on the test partition). Also, given its low inference
time (~6 ms per head), we consider our model usable even when paired with
medium-spec hardware (i.e. GTX 1060 GPU). * Code available at:
https://github.com/rafabs97/headpose_final * Demo video at:
https://www.youtube.com/watch?v=2UeuXh5DjAE

Computer Vision and Pattern Recognition;Artificial Intelligence