Hierarchical Multi-Scale Patch Attention and Global Feature-Adaptive Fusion for Robust Occluded Face Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Occluded face recognition remains a challenging problem in biometric identification, where real-world obstructions such as masks, sunglass, scarves, and hands obscure key facial features. To address this, We introduce a dual-branch architecture that combines Local Multi-Patch Attention Module (LMPAM) for extracting localized features with a Global Self-Attention Channel Module (GSACM) to enhance overall feature representation. The local branch utilizes multi-scale patch attention to adaptively emphasize visible facial regions, ensuring robust feature learning from unoccluded areas. Meanwhile, the global branch employs self-attention with channel recalibration to enhance discriminative features, capturing long-range dependencies while suppressing occlusion-induced noise. The two branches are integrated using Dynamic Weighted Local-Global Fusion (DW-LG), allowing the model to balance local and global information effectively. Unlike predefined occlusion-aware methods, our approach generalizes across occlusions of varying types, regions, and sizes and demonstrates robustness on multiple datasets with changes in illumination, pose, and facial expression—without requiring explicit localization. Extensive evaluations on CASIA-WebFace, LFW, and AR datasets demonstrate the effectiveness of our approach, achieving higher recognition performance under severe occlusion conditions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it