A new anchored normalization technique for score-level fusion in multimodal biometrie systems
Bibliographic record
Abstract
Dissimilarities in equal error rates (EERs) of multiple matchers heavily influence the performance of multi-biometric systems. A normalization technique aims at improving the recognition rate of such a system. In view of this, in this paper, an anchored normalization technique, referred to as improved anchored min-max (IAMM) technique for a multimodal biometric system, is developed. In the proposed technique, the anchor value is computed from the raw matching score sets corresponding to each of the modalities used in the system. This anchor value does not require a priori knowledge of the equal error rates and genuine/impostor score distributions of the individual matchers used in the system. It takes into account the average and variations of the score values that occur more than once in each score set. The performance of IAMM, in terms of EER and genuine acceptance rates @10% and @20% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric system using the proposed normalization technique is superior to that of the uni-biometric systems or to that of the system using the existing normalization techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".