Pyramid-guided multi-scale self-attention and channel–spatial refinement for occlusion-robust face recognition

Abstract Facial occlusion degrades face recognition by creating scale-inconsistent identity cues across visible regions and amplifying responses to irrelevant occluders. To address these coupled problems, this study proposes a pyramid-guided multi-scale attention framework based on scale alignment and reliability-aware feature refinement. Hierarchical features are first projected into a shared semantic space, after which local, intermediate, and global dependencies are adaptively weighted according to the available facial information. Channel–spatial refinement is then used to suppress unreliable responses from occluded regions, while an angular-margin objective preserves inter-identity separability from incomplete facial evidence. Experiments on CASIA-WebFace and occluded LFW show that the proposed method achieves an accuracy of 99.26% under clean conditions and 82.72% under occlusion, with an ROC-AUC of 0.8351. At 80% occlusion, the proposed method outperformed the strongest recent baseline, HMPA-GFAF, by 1.41% points and the direct Inception-ResNet-v1 + ArcFace baseline by 5.88% point. These results demonstrate that the proposed framework improves occlusion robustness without sacrificing clean-face recognition performance, indicating its practical potential for identity verification and access-control applications involving masks, glasses, and other partial facial occlusions.