2D Image head pose estimation via latent space regression under occlusion settings. (May 2023) - Celestino, José; Marques, Manuel; Nascimento, Jacinto C.; Costeira, João Paulo
Highlights: Occlusion robust HPE framework relying on latent space regression with multi-loss. Results surpass state-of-the-art methods in face images with occlusion and are similar without occlusion. Ablation study on occlusion size reveals 39% error decrease for large occlusions. Application in real scenario: feeding robot for people with upper body impairment. Synthetic occlusion generation and application to existing head pose datasets. 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.