Abstract: A novel Bayesian approach to face recognition based on wavelet transform and 2DPCA is proposed.The original image is decomposed into low frequency and high frequency sub-band images by applying wavelet transform，the 2DPCA algorithm is used to compute the eigenvector space of the face.Firstly Bayesian approach is used to the low-frequency sub-band，secondly for the selected top 5 match faces，Bayesian recognition is parallel processed using these high frequency sub-band images.The face recognition result is gained through weigh-adding arraying.Its efficiency and superiority are clarified by comparative experiment on a subset of FERET face data.