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Combined with wavelet transform, singular value decomposition and kernel principal component analysis, a method for face recognition is presented. Firstly, the wavelet transformation is used to reduce the dimension of the face picture. Then, SVD is used to subtract the features of the lowest resolution subimage, and the singular value feature vector is mapped onto the feature space with kpca and obtains nonlinear feature . Finally, face recognition can be realized according to BP neural network method. Experimental results on ORL and YALE face-databases show that the recognition rate by the proposed method is higher than that by KPCA, SVD, WT-KPCA and WT-SVD respectively.