Papers › 2D Image head pose estimation via latent space regression under occlusion settings
2D Image head pose estimation via latent space regression under occlusion settings
José Celestino, Manuel Marques, Jacinto C. Nascimento, João Paulo Costeira
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Head Pose Estimation | AFLW2000 | LSR | MAE | 4.412 | #14 of 25 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | LSR | MAE (trained with other data) | 3.519 | #7 of 29 | Archive leaderboard | report |
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