Fusing multiple matcher's outputs for secure human identification
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
Multimodal biometrics is an emerging area of research that aims at increasing the reliability of biometric systems through utilising more than one biometric in decision-making process. An effective fusion scheme plays a key role in combining the information presented by the multiple domain experts. Such information can be integrated at several distinct levels, such as sensor level, feature level, match score level, rank level and decision level. This paper describes the combination process of different monomodal expert through rank and decision fusion methods using iris, ear and face biometrics for secure human authentication. For rank-level fusion, plurality voting, Borda count and logistic regression approaches are employed and compared, and for decision-level fusion, AND/OR, majority voting, weighted majority voting and behavioural knowledge space approaches have been implemented and tested. The key contribution of the paper is in comparison of the recognition performance of the developed multimodal system for all of the above approaches. The results indicate that fusing individual modalities improve the overall performance of the biometric system and the logistic regression rank-level fusion results in the highest recognition performance even in the presence of low-quality data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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