Palmprint recognition method based on localized non-negative sparse coding

Abstract

To more effectively extract localized features of images,on the basis of the traditional Non-negative Sparse Coding(Hoyer NNSC) algorithm,a novel localized NNSC(LNNSC) algorithm with sparse constraint was proposed.This algorithm considered the sparse measure constraint of feature basis vectors and the maximized representativeness of features,and could obtain the strengthened localized image features.At the same time,this algorithm utilized the Laplace density model as the feature coefficients sparse punitive function to ensure an image's sparse structure.Furthermore,on the basis of feature extraction,by utilizing the Radial Basis Probabilistic Neural Networks(RBPNN),the palmprint recognition task could be implemented automatically.Compared with the palmprint recognition methods of Non-negative Matrix Factorization(NMF),Local NMF(LNMF) and Hoyer-NNSC,simulation results show that our method proposed here displays feasibility and practicality in palmprint recognition.