  Head pose estimation has become a crucial area of research in computer vision
given its usefulness in a wide range of applications, including robotics,
surveillance, or driver attention monitoring. One of the most difficult
challenges in this field is managing head occlusions that frequently take place
in real-world scenarios. In this paper, we propose a novel and efficient
framework that is robust in real world head occlusion scenarios. In particular,
we propose an unsupervised latent embedding clustering with regression and
classification components for each pose angle. The model optimizes latent
feature representations for occluded and non-occluded images through a
clustering term while improving fine-grained angle predictions. Experimental
evaluation on in-the-wild head pose benchmark datasets reveal competitive
performance in comparison to state-of-the-art methodologies with the advantage
of having a significant data reduction. We observe a substantial improvement in
occluded head pose estimation. Also, an ablation study is conducted to
ascertain the impact of the clustering term within our proposed framework.
