Abstract: The algorithm of face recognition based on kernel principal component analysis（KPCA）can abstract nonlinear features of image and can get better performance under less sample training conditions. Not all nonlinear features are beneficial to the recognition. The superabundant unrelated features may reduce the recognition performance. The image was transformed by wavelet transformation for its redundancy, which not only has improved the accuracy of recognition but has reduced the demand for computer hardware of the algorithm. A pretreatment strategy that can reduce image gradation was developed in order to restrain upper sensitivity of KPCA to the change of illumination. The experimental results based on ORL-DATABASE show that the above-mentioned algorithm allows faster training speed and higher accuracy of recognition than traditional ones.

Key words: kernel principal component analysis , wavelet transform, face recognition