Decision Fusion for Multimodal Biometrics Using Social Network Analysis
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
This paper presents for the first time decision fusion for multimodal biometric system using social network analysis (SNA). The main challenge in the design of biometric systems, at present, lies in unavailability of high-quality data to ensure consistently high recognition results. Resorting to multimodal biometric partially solves the problem, however, issues with dimensionality reduction, classifier selection, and aggregated decision making remain. The presented methodology successfully overcomes the problem through employing novel decision fusion using SNA. While several types of feature extractors can be used to reduce the dimension and identify significant features, we chose the Fisher Linear Discriminant Analysis as one of the most efficient methods. Social networks are constructed based on similarity and correlation of features among the classes. The final classification result is generated based on the two levels of decision fusion methods. At the first level, individual biometrics (face or ear or signature) are classified using matching score methodology. SNA is used to reinforce the confidence level of the classifier to reduce the error rate. In the second level, outcomes of classification based on individual biometrics are fused together to obtain the final decision.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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