Multi-feature Multimodal Biometric Recognition Based on Quaternion Locality Preserving Projection

Abstract

This paper proposed Quaternion locality preserving projection (QLPP) for multi-feature multimodal biometric recognition. Multi-features fill the real part or the three imaginary parts of quaternion to constitute the quaternion fusion features. In quaternion division ring, QLPP extracts the local information and finds essential manifold structure of the quaternion fusion features. Deferent from Quaternion principal component analysis (QPCA) and Quaternion fisher discriminant analysis (QFDA), QLPP takes advantage of the optimal linear approximations to find the nonlinear manifold structures. Two experiments are designed: one fuses four features from two biometric modalities, and the other fuses three features from three biometric modalities. The experimental results show the proposed algorithm achieves much better performance than the unimodal biometric algorithms, the traditional feature level fusion methods(weighted sum rule and series rule) and two quaternion representation methods(QPCA and QFDA).