Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape
Bibliographic record
Abstract
Ocular biometrics refers to the use of features of the eye for person recognition. For instance, the unique and stable texture of the iris has been recognised as a powerful ocular biometric characteristic. In this study, the authors propose to improve biometric authentication with a multimodal ocular biometric system based on the iris pattern and the three‐dimensional shape of the cornea. They show how the cornea can be used as a biometric trait for person recognition and then, they propose an intra‐ocular fusion with iris features to improve the overall performance of the system. Feature extraction was done by modelling the shape of the cornea with a Zernike polynomial expansion. Then the best linear combinations of Zernike coefficients were found with linear discriminant analysis and used as biometric identifier. The iris texture was analysed with a typical methodology using Gabor filtering and phase encoding. The fusion was performed at the matching score level using min, max, sum and weighted‐sum rule. The experimental results on a new database constructed for this bi‐modal study showed impressive performance of the proposed ocular biometric system with equal error rate decreasing to 0% with the weighted‐sum rule.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".