Bayesian Face Recognition Using 2DPCA

Bayesian subspace analysis has been successfully applied in face recognition. However, the direct application of the algorithm is much more computationally intensive. In this paper, a novel Bayesian approach to face recognition based on 2DPCA is proposed. Firstly the system uses 2DPCA to select the 10 top-ranked candidate images, secondly these candidate images and each testing image are decomposed into low frequency and high frequency sub-band images by applying wavelet transform, Bayesian recognition is parallel processed using these sub-band images. The face recognition result was gained through weigh-adding arraying. Its efficiency and superiority are clarified by comparative experiment on a subset of FERET face data.