Palmprint identification combining hierarchical multi-scale complete LBP and weighted SRC

Abstract

Palmprint is one of the most reliable biometrics and has been widely used for human identification due to its high recognition accuracy and convenience for practical application. But the existing palmprint-based human identification system often suffers from image misalignment, pixel corruption and much computational time on the large database. An effective palmprint recognition method is proposed by combining hierarchical multi-scale complete local binary pattern (HMS-CLBP) and weighted sparse representation-based classification (WSRC). The hierarchical multi-scale local invariant texture features are extracted firstly from each palmprint by multi-scale local binary pattern (MS-LBP) and multi-scale complete local binary pattern (MS-CLBP) and are concatenated into one hierarchical multi-scale fusion feature vector. Then, WSRC is constructed by the Gaussian kernel distance, and use the Gaussian kernel distances between the fusion feature vectors of the training and testing samples. Finally, the sparse decomposition of testing samples is implemented by solving the optimization problem based on l1 norm, and the palmprints are recognized by the minimum residuals. The proposed method inherits the advantages of CLBP and WSRC and has good rotation, illumination and occlusion invariance. The results on the PolyU and CASIA palmprint databases illustrate the good performance and rationale interpretation of the proposed method.