Papers › A Data-Driven Approach to Improve 3D Head-Pose Estimation
A Data-Driven Approach to Improve 3D Head-Pose Estimation
Nima Aghli, Eraldo Ribeiro
Head-pose estimation from images is an important research topic in computer vision. Its many applications include detecting focus of attention, tracking driver behavior, and human-computer interaction. Recent research on head-pose estimation has focused on developing models based on deep convolutional neural networks (CNNs). These models are trained using transfer-learning and image augmentation to achieve better initiation states and robustness against occlusions. However, methods that use transfer-learning networks are usually aimed at general image recognition and offer no in-depth study of transfer learning from more task-related networks. Additionally, for the head-pose estimation, robustness against heavy occlusion, and noise such as motion blur and low-brightness are vital. In this paper, we propose a new image-augmentation approach that significantly improves the estimation accuracy of the head-pose model. We also propose a task-related weight initialization to further improve the estimation accuracy by studying internal activations of models trained for face-related tasks such as face-recognition. We test our head-pose estimation model on three challenging test sets and achieve better results to state-of-the-art methods.
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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 | DDD-Pose | MAE | 4.22 | #13 of 25 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | DDD-Pose | MAE (trained with BIWI data) | 2.80 | #16 of 29 | Archive leaderboard | report |
| Head Pose Estimation | BIWI | DDD-Pose | MAE (trained with other data) | 4.52 | #16 of 29 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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