Iterative Weighted Non-Smooth Non-Negative Matrix Factorization For Face Recognition

Non-negative matrix factorization (nmf) is a part-based image representation method. it comes from the intuitive idea that entire face image can be constructed by combining several parts. in this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “iterative weighted non-smooth non-negative matrix factorization” (iwns-nmf). a new cost function is proposed in order to incorporate sparsity which is controlled by a specific parameter and weights of feature coefficients. this method extracts highly localized patterns, which generally improves the capability of face recognition. after extracting patterns by iwns-nmf, we use principle component analysis to reduce dimension for classification by linear svm. the recognition rates on orl, yale and jaffe datasets were 97.5, 93.33 and 87.8%, respectively. comparisons to the related methods in the literature indicate that the proposed iwns-nmf method achieves higher face recognition performance than nmf, ns-nmf, local nmf and snmf.