A 3D Palmprint Recognition Method based on Local Sparse Representation and Weighted Shape Index Feature

A palmprint recognition method is proposed by local sparse representation. The method consists of two problems: palmprint feature extraction problem and palmprint recognition problem. In the aspect of feature extraction, the weighted shape index feature is adopted to describe three-dimensional surface. As for the recognition problem, the two-stage classification method is proposed by local sparse representation. Firstly, the similarity is used to construct the sample subset, which reserves candidate classed of the test data set. Secondly, the sparse coding classifier is used to obtain the palmprint category. The experimental results and comparisons on the Hong Polytechnic University palm data set verify that the proposed approach has better effectiveness than the traditional methods.