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.

本文提出了一个新颖而高效的框架，通过无监督的潜在嵌入聚类与回归和分类组件来优化遮挡和非遮挡图像的潜在特征表示，并改善细粒度角度预测，在野外头部姿势数据集上得到了与最先进方法相当的竞争性能，并具有显著的数据减少优势。我们观察到遮挡头部姿势估计方面有显著改进，并进行了消融实验来验证我们提出的框架中聚类项的影响。