A Novel Index-Based Rank Fusion Method for Occluded Ear Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Ear biometrics are often partially or fully occluded by hair, earrings, headphones, hat/cap, scarf, and other obstacles. Occurrence of occlusion during identification stage may cause significant information loss, which deteriorates recognition performance. In this paper, we proposed a novel index-based rank fusion method for ear recognition that can utilize occlusion information adaptively during identification stage to decide on a person's identity. In the proposed method, feature sets are selected and weighted according to the proportion of occlusion during identification time. Our experimental results on wide variety of real as well as synthetically occluded ears demonstrate that the proposed adaptive feature selection and fusion method significantly improves the recognition performance of occluded ears.
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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 | 0.001 | 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 | 0.000 |
| 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