Occluded Face Recognition Through Enhanced Self-Attention and Cross-Attention Mechanisms
Bibliographic record
Abstract
Occluded face recognition remains a critical challenge in biometric systems and real-world applications, where obscured facial features can significantly hinder identification accuracy. To address this issue, we present an approach that enhances the ResNet18 architecture by integrating positional encoding (PE) with multi-head self-attention (MHSA) and crossattention (CAM) mechanisms. This hybrid design enables the model to selectively focus on critical facial regions while mitigating the effects of occlusions. Evaluated on the Extended Yale B dataset with synthetic occlusions and the AR dataset with real-world occlusions, the proposed model demonstrates remarkable improvements in accuracy and robustness. Our method surpasses state-of-the-art techniques, particularly in scenarios with partial facial occlusions. This work improves occluded face recognition and supports the development of reliable next-generation biometric systems.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".