Enhancing security through a hybrid multibiometric system
Bibliographic record
Abstract
Biometric user authentication techniques for security and access control have evoked an enormous interest by science, industry and society in the last two decades. Scientist and researchers have constantly pursued the technology for automated confirmation of the identity of subjects based on measurements of physiological or behavioral traits of humans. But even the best single biometric system suffers from spoof attacks, intra-class variability, noise, susceptibility etc. To address this issue, we develop a hybrid multibiometric system which integrates multi-algorithm and multi-modal approaches of multibiometric system and use bilevel fusion to combine biometric information. We use face, ear and signature biometric traits which are first classified by three classification techniques-multilayer perceptron, Fisher-image and Bayesian network. The outcomes of these classifiers for face are fused by rank fusion method. Outcomes for ear and signature are also fused similarly. The second level fusion occurs when we combine the results of these three rank fusion methods' outcomes for face, ear and signature with decision fusion method. We use Borda count and Borda fuse approaches for rank fusion and majority voting, weighted majority voting and behavioral knowledge space approaches for decision fusion. The final results indicate that this hybrid multi biometric system outperforms the single biometric systems build on the same data using the same classification algorithms. This system can be effectively used in law enforcement or homeland security department or for commercial purposes.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".