Weighted Hybrid Fusion for Multimodal Biometric Recognition System
Bibliographic record
Abstract
In this paper, first, a new fusion technique, referred to as hybrid fusion (HBF) technique, based on feature-level fusion and the best unimodal system for multimodal biometric system recognition, is proposed. Secondly, a new weighting technique, referred to as mean-extrema based confidence weighting (MEBCW) technique, based on the scores obtained from feature-level fusion and the best unimodal system, is proposed. Finally, a weighted hybrid fusion, referred to as weighted hybrid fusion (WHBF) technique, is developed by incorporating MEBCW in HBF, in order to improve the overall recognition rate of a multimodal biometric system. The performance of the proposed method, in terms of equal error rate and genuine acceptance rates @5.3% and @7.2% false acceptance rates, is evaluated on a multi-biometric system. The experimental results show that the performance of a multi-biometric systems using the proposed fusions is superior to that of the uni-biometric systems or to that of the system using existing level of fusions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".