Wavelet-based Non-negative Matrix Factorization with Sparseness Constraints for Face Recognition
Yi Zhang · Jisuanji yingyong yanjiu · 2006
This paper combines Wavelet Transformation(WT),Non-negative Matrix Factorization with sparseness constraints(NMFs),and Fisher's Linear Discriminant(FLD) to extract features for face recognition.Wavelet transformation is used to decompose face images and for choosing the lowest resolution sub-band coefficients so that the substantial facial features can be captured and the computational complexity can be reduced.NMFs can control sparseness explicitly and find parts-based representations for face images.FLD plays the role of forming well-separated classes in a low-dimensional subspace.Extensive experiments are carried out to illustrate the proposed combine face recognition method by using the ORL face database.The experimental results show that the method has robust high-performance against varying illumination,facial expression and part occlusion.