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Generalized Face Recognition With Occlusion
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AI
DOI：10.1109/LSP.2026.3700555.png)
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Abstract
En 中文
Face recognition under occlusion remains challenging due to masks, glasses, and other real-world obstructions that partially conceal facial information. Existing approaches typically rely on training with one or more predefined occlusion types, which limits their ability to generalize to unseen scenarios. In this letter, we propose Generalized Face Recognition with Occlusion (GFRO), an occlusion-robust framework that generalizes to diverse occlusion patterns without requiring occlusion-specific training data. GFRO is trained on partial facial views cropped from complete face images using a cross-entropy loss to learn generic representations across different partial views. A dual mixture-of-experts aggregator is then introduced to refine and integrate features from multiple branches, each handling a specific partial-view representation. Optimized with a clean–noisy contrastive loss, the aggregator aligns partial-face features with complete-face features, where each branch contains experts specializing in complementary partial-view information. Extensive experiments on multiple datasets demonstrate that GFRO generalizes effectively to both real and synthetic occlusion scenarios and achieves state-of-the-art performance compared with methods trained on occlusion-specific data under the same occlusion conditions.
Keywords:
Contrastive learning
face verification
mixture of experts
occluded face recognition
partial-view learning
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