Robust face recognition under illumination variations is an important and challenging task in a face recognition system, particularly for face recognition in the wild. In this paper, a face image preprocessing approach, called spatial adaptive shadow compensation (SASC), is proposed to eliminate shadows in the face image due to different lighting directions. First, spatial adaptive histogram equalization (SAHE), which uses face intensity prior model, is proposed to enhance the contrast of each local face region without generating visible noises in smooth face areas. Adaptive shadow compensation (ASC), which performs shadow compensation in each local image block, is then used to produce a wellcompensated face image appropriate for face feature extraction and recognition. Finally, null-space linear discriminant analysis (NLDA) is employed to extract discriminant features from SASC compensated images. Experiments performed on the Yale B, Yale B extended, and CMU PIE face databases have shown that the proposed SASC always yields the best face recognition accuracy. That is, SASC is more robust to face recognition under illumination variations than other shadow compensation approaches.
Vector Quantization has been applied to low-bit-rate speech and image compression. One of the most serious problems for vector quantization is the high computational complexity of searching for the closest codeword in the codebook design and encoding processes. To overcome this problem, a fast algorithm, under the assumption that the distortion is measured by the squared Euclidean distance, will be proposed to search for the closest codeword to an input vector. Using the means and variances of codewords, the algorithm can reject many codewords that are impossible to be candidates for the closest codeword to the input vector and hence save a great deal of computation time. Experimental results confirm the effectiveness of the proposed method.