Face Recognition Based on Wavelet Multiscale Singular-value Decomposition

As a matrix decomposition method,singular value decomposition(SVD) can be used to extract algebraic features from images.The SV features of images have many good properties such as stability,geometric invariance,and insensitiveness to noise.However,it is difficult to achieve high recognition rate by only using one scale SV features in ima-ge recognition.Based on the wavelet transforms and SVD,this paper proposed an image feature extraction method which combines multiple scale SV features of wavelet sub-band images.The best recognition rates on three face databses(ORL,YALE,and JAFFE) are 82.11%,100%,and 95.68% respectively,which are higher than existing SVD based approaches.