Abstract

In this paper, an efficient feature extraction method based on local statistics features of block difference of inverse probabilities (BDIP) and the wavelet transform is proposed for face recognition. In the proposed method, the BDIPs are first computed in a face in order to overcome the variation of illumination and facial expressions. The obtained BDIP image is then decomposed into wavelet subbands. In order to reduce the dimensionality of the feature vector, each BDIP subband is partitioned into a set of blocks. The means and variances are then calculated from all the blocks in each subband and are fused into a feature vector. Experimental results on ORL and FERET databases show that the proposed method achieves higher recognition accuracies than the wavelet-based methods with higher dimensionality reduction of the feature vector. It also outperforms the other well known methods such as PCA and the DCT with the zigzag scanning.