Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization

In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix factorization could be improved by the method proposed. The proposed face recognition method combines wavelet kernel non-negative matrix factorization and RBF network. Extensive experimental results on ORL and YALE face database show that the suggested method possesses much stronger analysis capability than the comparative methods. Compared with PCA, non-negative matrix factorization, kernel PCA and independent component analysis, the proposed face recognition method with WKNMF and RBF achieves over 10 % improvement in recognition accuracy.