Occlusion-Aware Face Recognition via Adaptive Local Feature Fusion and Identity-Guided Contrastive Learning

Partial occlusion can substantially impair the accuracy and stability of face recognition systems. Although existing methods perform well on unobstructed face images, their performance often degrades under partial occlusion, because occluded regions may obscure discriminative identity cues and introduce noise into feature representations. To address this issue, this paper proposes an occlusion-aware face recognition framework that integrates lightweight feature extraction, local-region reliability modeling, adaptive feature fusion, and joint loss optimization. Specifically, the face is divided into three sub-regions according to common occlusion patterns, and an MLP-based module is used to estimate the reliability of each region. The estimated reliability weights are then used to adaptively fuse local features, thereby emphasizing visible and discriminative regions. In addition, a joint loss combining ArcFace and InfoNCE is constructed to enhance inter-class separability and intra-class feature consistency. Experimental results under masks, hats, sunglasses, and random occlusion conditions show that the proposed method achieves a recognition accuracy of 92.3%. Compared with ArcFace, CurricularFace, and AdaFace, the proposed method improves accuracy by 9.9%, 6.5%, and 4.1%, respectively. In addition, the FAR is reduced by 5.8%, 4.9%, and 3.7%, respectively, while the FRR is reduced by 2.2%, 6.5%, and 1.3%, respectively. These results demonstrate that the proposed framework effectively enhances the robustness of face recognition under partial occlusion.