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This paper proposes a novel illumination normalization approach with low computation complexity for face recognition. In this proposed method, a block-wise Walsh-Hadamard transform (WHT) is employed in the logarithm domain. An appropriate number of low-frequency WHT coefficients are zeroed to compensate for illumination variations. Experiments on different databases demonstrate that the proposed method obtains results comparable to those of conventional Discrete Cosine Transform method but with a higher efficiency. It also achieves better performances for cases with larger illumination variations. Furthermore, both analytical proof and experimental results demonstrate that principal component analysis (PCA) can be directly implemented in the WHT domain.