  Head orientation is a challenging Computer Vision problem that has been
extensively researched having a wide variety of applications. However, current
state-of-the-art systems still underperform in the presence of occlusions and
are unreliable for many task applications in such scenarios. This work proposes
a novel deep learning approach for the problem of head pose estimation under
occlusions. The strategy is based on latent space regression as a fundamental
key to better structure the problem for occluded scenarios. Our model surpasses
several state-of-the-art methodologies for occluded HPE, and achieves similar
accuracy for non-occluded scenarios. We demonstrate the usefulness of the
proposed approach with: (i) two synthetically occluded versions of the BIWI and
AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii)
a real-life application to human-robot interaction scenarios where face
occlusions often occur. Specifically, the autonomous feeding from a robotic
arm.
