Face Recognition Based on Discriminant Sparsity Neighborhood Preserving Embedding

As the key issues of face recognition,a novel face extraction algorithm is proposed in this paper. A discriminant sparsity neighborhood preserving embedding algorithm( DSNPE) is proposed based on sparse representation and neighborhood embedding. The algorithm can create neighborhood graph with sparse representation,including within-neighborhood graph and between-neighborhood graph,respectively discusses the problems about within-class compactness and between-class sparseness. At the same time the DSNPE algorithm can take advantage of the sample category information better,so this algorithm has supervision. The objective function of DSNPE algorithm is established based on the maximum distance between standards,and its basic flow is described. Finally,the related experiment is conducted with comparison of SPP,NPE,LPP,MMC,LDA and PCA algorithm on Yale,ORL and AR face databases,the experimental results show that the discriminant sparsity neighborhood preserving Embedding algorithm has better performance of face recognition.