Abstract
In this paper, an efficient feature extraction method based on block difference of inverse probabilities (BDIP) and DCT pyramid for face recognition is proposed. The BDIP is first computed in a face image in order to overcome the variation of illumination. The extracted BDIP image is then decomposed using DCT pyramid. The DCT pyramid decomposes the BDIP image into an approximation subband and a set of 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 and the other well known methods such as the PCA and the block-based DCT with the zigzag scanning.
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