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Face recognition (FR) is a challenging issue due to variations in pose, illumination, and expression. In this paper, a face recognition system with low-memory requirement and accurate recognition is presented. It is based on extraction of features with the DCT pyramid, in contrast to the conventional method of wavelet decomposition. 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 in terms of accuracy and memory requirement.