Multimodal Face and Iris Recognition System Based on Local Feature Extraction and Binary Bat Algorithm
Bibliographic record
Abstract
The Multimodal Biometric System (MBS) is widely utilized in security due to its superior performance compared to Unimodal Biometric Systems (UBSs).However, developing an MBS with high accuracy and acceptable complexity is still of prime interest.This paper proposes an innovative MBS based on advanced feature extraction and selection methods to improve face-iris recognition.The proposed method introduces three algorithms for Local Feature Extraction (LFE) of both faces and irises, effectively capturing detailed image characteristics.These extracted local features are fused in a unified matrix to provide a better description of modalities.In addition, the concatenated data undergoes dimensionality reduction by using a binary bat algorithm (BBA) intended for selecting the most significant features required for iris-face recognition.This contributes to improving the recognition accuracy and computational efficiency.The BBA is adopted due to its robust global optimization capabilities and adaptive exploration-exploitation balance.For classification at the score level, the extreme learning machine (ELM) is suggested, which demonstrates superior performance over the support vector machine (SVM) and genetic algorithm (GA).The system's robustness is validated using the CASIA Iris distance database, containing high-resolution images of both left and right eyes.The experimental results show significant improvements over (UBSs), underscoring the effectiveness of the designed MBS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".