Quaternion Kernel Fisher Discriminant Analysis for Feature‐Level Multimodal Biometric Recognition

Abstract

Quaternion kernel Fisher discriminant analysis (QKFDA) is proposed for feature level multimodal biometric recognition. In quaternion division ring, QKFDA extracts the most discriminative information from the quaternion fusion feature sets by maximizing the betweenclass variance while minimizing the within-class variance. A complete two-phases framework of QKFDA is developed: Quaternion kernel principal component analysis (QKPCA) plus Quaternion linear discriminant analysis(QLDA). Two experiments are designed: experiment I fuses four different features of face and plamprint, experiment II fuses three different features of face, plamprint and signature. The experimental results show that QKFDA is superior to both traditional feature fusion methods (series rule and weighted sum rule)and other quaternion feature fusion methods (QPCA, QFDA, QLPP and QKPCA).