2D Image head pose estimation via latent space regression under occlusion settings
Abstract
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.
Cite
@article{arxiv.2311.06038,
title = {2D Image head pose estimation via latent space regression under occlusion settings},
author = {José Celestino and Manuel Marques and Jacinto C. Nascimento and João Paulo Costeira},
journal= {arXiv preprint arXiv:2311.06038},
year = {2023}
}