Enhancing Biometric Authentication Efficiency: A Hybrid Approach Exploiting Iris Modality and Leveraging One-Class SVM
Bibliographic record
Abstract
Biometric characteristics play a vital role in the authentication and identification process, particularly in devel-oping resilient and secure systems with the main objective of safeguarding private data and ensuring secure access. Typically, two different modalities are commonly utilized: behavioural and physiological. Within the realm of physiological modalities, the iris stands out prominently due to its exceptional uniqueness and stability, rendering it exceedingly valuable in the context of security. This paper introduces a novel biometric-based authen-tication system that utilizes the iris as a modality. The proposed system is hybrid as it combines a pre-trained Convolutional Neural Network for feature extraction with the efficiency of a One-Class Support Vector classifier. To optimize the classification task and generate relevant features, Linear Discriminant Analysis is also employed. We evaluated the performance of the proposed system using two publicly available benchmark datasets. The system yield promising performance, achieving an accuracy of 99.07% and 99.68% with the CASIA-Iris-V1 and CASIA-Iris-Interval datasets, respectively.
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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 | 0.001 |
| 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 | 0.000 |
| 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 | 0.002 |
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".