Papers › Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks
Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks
Meilu Zhu, Daming Shi, Mingjie Zheng, Muhammad Sadiq
In this paper, we present a simple and effective framework called Occlusion-adaptive Deep Networks (ODN) with the purpose of solving the occlusion problem for facial landmark detection. In this model, the occlusion probability of each position in high-level features are inferred by a distillation module that can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. The occlusion probability serves as the adaptive weight on high-level features to reduce the impact of occlusion and obtain clean feature representation. Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. To obtain exhaustive and complete feature representation, it is vital that we leverage a low-rank learning module to recover lost features. Considering that facial geometric characteristics are conducive to the low-rank module to recover lost features, we propose a geometry-aware module to excavate geometric relationships between different facial components. Depending on the synergistic effect of three modules, the proposed network achieves better performance in comparison to state-of-the-art methods on challenging benchmark datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Face Alignment | 300W | ODN | NME_inter-ocular (%, Challenge) | 6.67 | #39 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | ODN | NME_inter-ocular (%, Common) | 3.56 | #39 of 48 | Archive leaderboard | report |
| Face Alignment | 300W | ODN | NME_inter-ocular (%, Full) | 4.17 | #39 of 48 | 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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