Combining Modified LBP and Weighted SRC for Palmprint Recognition

Abstract

In order to extract invariant features in the palmprint transformation of scale, rotation and affine distortion, a coarse-to-fine palmprint recognition method is proposed by combining the weighted adaptive center symmetric local binary pattern (WACS-LBP) and weighted sparse representation based classification (WSRC). The method consists of coarse and fine stages. In the coarse stage, using the similarity between the test sample and one sample of each training class, most of the training classes could be excluded and a small number of candidate classes of the test sample are reserved. Thus, the original classification problem becomes clear and simple. In the fine stage, the robust rotation invariant weighted histogram feature vector is extracted from each candidate sample and the test sample by WACS-LBP, and the weighted sparse representation optimal problem is constructed by the similarity between the test sample and each candidate training sample, and the test sample is recognized by the minimum residual. The proposed method is tested and compared with the existing algorithms on the PolyU and CASIA database. The experimental results illustrate better performance and rationale interpretation of the proposed method.