Multimodal Face and Iris Recognition System Based on Local Feature Extraction and Binary Bat Algorithm

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 advan...

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Veröffentlicht in:Traitement du signal Jg. 42; H. 3; S. 1759
Hauptverfasser: Bouzouina, Sidahmed Yacine, Hamami, Latifa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Edmonton International Information and Engineering Technology Association (IIETA) 01.06.2025
Schlagworte:
Accuracy
Biometric identification
Biometric recognition systems
Biometrics
Face recognition
Feature extraction
Feature selection
Genetic algorithms
Global optimization
Image resolution
Machine learning
Methods
Optimization algorithms
Support vector machines
ISSN:0765-0019, 1958-5608
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:0765-0019
1958-5608
DOI:10.18280/ts.420344