Feature selection for support vector machine-based face-iris multimodal biometric system

Multimodal biometric can overcome the limitation possessed by single biometric trait and give better classification accuracy. This paper proposes face-iris multimodal biometric system based on fusion at matching score level using support vector machine (SVM). The performances of face and iris recognition can be enhanced using a proposed feature selection method to select an optimal subset of features. Besides, a simple computation speed-up method is proposed for SVM. The results show that the proposed feature selection method is able improve the classification accuracy in terms of total error rate. The support vector machine-based fusion method also gave very promising results.

► This paper proposes a face-iris multimodal biometric system based on fusion at matching score level using support vector machine.
► A feature selection method is proposed to enhance the performance of face and iris recognition.
► A simple computation speed-up technique is proposed for SVM.
► The results show that the proposed feature selection method is able to improve classification accuracy in term of total error rate.
► The propose SVM-based fusion method gave promising results.