Palmprint recognition method based on score level fusion

Abstract

Different palmprint recognition methods have different advantages. The texture- and feature-based palmprint recognition methods can well exploit the minutiae of the palmprint but are not very robust to the possible variation such as the rotation and shift of the palm. The representation-based palmprint recognition method can well take advantage of the holistic information but seems not to be able to fully exploit the minutiae of the palmprint. In this paper, we propose to fuse the competitive coding method and two-phase test sample sparse representation (TPTSR) method for palmprint recognition. As one of representation-based methods, TPTSR method takes the whole palmprint image as the input and determines the contribution of the training samples of each class in representing the test sample. TPTSR also uses the contribution to calculate the similarities between the test sample and every class. The competitive coding method is a feature-based method and is highly complementary with TPTSR. We use a weighted fusion scheme to combine the matching scores generated from TPTSR and the competitive coding method. The experimental results show that the proposed method can obtain a very high classification accuracy and outperforms both TPTSR and the competitive coding method.