Sparse Non-negative Matrix Factorization Based on Spatial Pyramid Matching for Face Recognition

Abstract

The non-negative matrix factorization (NMF) is a part-Based image representation method which allows only additive combinations of non-negative basis components. NMF has been widely used as a dimensionality reduction technique to solve problems in computer vision and pattern recognition fields. The sparse representation and spatial information of image are also important, however, existing NMF methods do not take these two aspects into consideration simultaneously. In this paper, we propose a novel NMF method with spatial information for face recognition, which is called sparse non-negative matrix factorization Based on spatial pyramid matching (SNMFSPM). Experimental results on several benchmark databases show that the proposed scheme outperforms some classical methods.