Face recognition based on DCT pyramid feature extraction

Face recognition is a challenging problem due to variations in pose, illumination, and expression. Most techniques that can provide effective feature representation are based on wavelet transform. In this paper, an efficient feature extraction method based on DCT pyramid for face recognition is proposed. The DCT pyramid performed on each face image decomposes it into an approximation subband and the reversed L-shape blocks containing the high frequency coefficients of the DCT pyramid. A set of simple block-based statistical measures is calculated from the extracted DCT pyramid subbands. This set of statistical measures is an efficient way of reducing the dimensionality of the feature vectors. Experimental results on the standard ORL and FERET databases show that the proposed method achieves more accurate face recognition than the wavelet-based methods. Moreover, it outperforms the other well known methods such as PCA and the block-based DCT with the zigzag scanning.