Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robust Multi-modal and Multi-unit Feature Level Fusion of Face and Iris Biometrics
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
PSO versus AdaBoost for feature selection in multimodal biometrics
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
Feature selection for support vector machine-based face-iris multimodal biometric system
Expert Systems with Applications: An International Journal
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This paper presents a multibiometric system that integrates the iris, palmprint, and fingerprint features based on the fusion at feature level. The novelty of this research effort is that we propose a feature subset selection scheme based on Particle Swarm Optimization (PSO) with a new fitness function that minimizes the Recognition Error (RR), False Accept Rate (FAR), and Feature Subset Size (FSS). Furthermore, we apply a Distance Regularized Level Set (DRLS)-based iris segmentation procedure, which maintains the regularity of the level set function intrinsically during the curve evolution process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Experimental results indicate that the proposed approach increases biometric recognition accuracies compared to that produced by single modal biometrics.