Rank level fusion of multimodal cancelable biometrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancelable biometrics is newly emerged biometric technology that can provide the protection over different attacks to a biometric system. In this paper, we have presented a multilevel random projection on face and ear biometric traits. The multiple random projections are conducted using multiple random projection matrixes. From multiple random projections, we have generated multiple templates for biometric authentication. Therefore, proposed method can provide better template security and better feature quality. Multiple cancelable templates are used for recognition purpose and rank level fusion is applied to generate final decision from multiple ranks. As per our knowledge, we have applied rank level fusion on cancelable multimodal biometric system for the first time. A detailed validation and the performance analysis of the proposed algorithm on a virtual multimodal cancelable face and ear database are presented.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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