Enhanced Security in Biometrics: A Cancelable Multi-Instance Iris Authentication Utilizing Quotient Filter
Bibliographic record
Abstract
Biometric-based authentication systems (BAS) can provide a strong security guarantee regarding the identity of users over traditional authentication systems.The iris of the eye is widely acknowledged as one of the most robust biometrics due to its exceptional performance.Despite this, templates used in traditional iris recognition systems remain unprotected, rendering them highly susceptible to various security and privacy breaches.However, several cancelable biometric schemes being introduced but at the expense of substantially decreased accuracy performance and increased computational time.To address this, we propose a cancelable multi-instance iris authentication system utilizing a quotient filter (CMAQF).The purpose of the quotient filter in CMAQF is to distort the biometric information without compromising the accuracy.Modified local random projection is applied on the fused iris template to generate the reduced template results in less authentication time.Experiments have been conducted on publicly available iris databases to assess the efficiency of CMAQF.The experimental results conclude that CMAQF achieves reasonable performance compared to existing methods, satisfying the properties of irreversibility, diversity, and revocability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".