Iris recognition using genetic algorithms and asymmetrical SVMs
Bibliographic record
Abstract
With the increasing demand for enhanced security, iris biometrics-based personal identification has become an interesting research topic in the field of pattern recognition. While most state-of-the-art iris recognition algorithms are focused on preprocessing iris images, important new directions have been identified recently in iris biometrics research. These include optimal feature selection and iris pattern classification. In this paper, we propose an iris recognition scheme based on Genetic Algorithms (GAs) and asymmetrical Support Vector Machines (SVMs). Instead of using the whole iris region, we elicit the iris information between the collarette and the pupillary boundaries to suppress effects of eyelids and eyelashes occlusions, and pupil dilation, and to minimize the matching error. To select the optimal feature subset together with increasing the overall recognition accuracy, we apply GAs with a new fitness function. The traditional SVMs are modified into asymmetrical SVMs to handle: (1) highly unbalanced sample proportion between two classes, and 2) different types of misclassification error that lead to different misclassification losses. Furthermore, the parameters of SVMs are optimized in order to improve the generalization performance. The proposed technique is computationally effective, with recognition rates of 97.80% and 95.70% on the Iris Challenge Evaluation (ICE) and the West Virginia University (WVU) iris 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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".