Face Recognition based on Linear Projective Non-negative Matrix Factorization with Kullback-Leibler Divergence
Lirui Hu, Siyuan Jia, Ning Wu · Journal of Physics Conference Series · 2020
Abstract In order to solve the problem of feature extraction and classification of Non-negative Matrix Factorization (NMF) in face recognition, a Linear Projective Non-negative Matrix Factorization method based on Kullback-Leibler divergence (LP-NMF-DIV) is proposed. In LP-NMF-DIV, an objective function of Kullback-Leibler divergence is considered. The Taylor series expansion and the Newton iteration formula are used to find the root formula. The iterative algorithm of basis matrix and linear transformation matrix are derived, and the convergence of the algorithm is proved. The results of experimental show that the algorithm is convergent. In face recognition, compared with some typical NMF methods, the algorithm has high recognition accuracy; when the ranks of the basis matrices are set to different values, the algorithm is stable. The method for LP-NMF-DIV is effective.