Local Application of Non Negative Matrix Factorization Algorithm in Face Recognition

Face recognition is a challenging issue in the field of multi-science, the main contents of the research is how to make computer have the ability of face recognition face recognition technology involved in a lot, which is a key feature extraction and classification method, this paper focuses on the study of related theory. Non-negative matrix factorization trapped MF) algorithm and local non-negative matrix factorization (LNMF) algorithm is a feature extraction method based on local features, has been successfully used in face recognition "but NMF algorithm in face recognition rate is low, although LNMF algorithm to a certain extent, improve the recognition rate, but its price is to increase the number of iterations. In addition, the two algorithms have failed to solve good nonlinear separable problems "the kernel method combined with LNMF algorithm, the kernel local non-negative matrix factorization (KLNMF) algorithm, the first by a nonlinear transformation of the original space to high-dimensional space, making samples linearly separable, and then use the LNMF algorithm to extract face features. In the classification part, paper presents the decision rules of classification of their own, and design based on the NMF subspace classifier.