The objective of this work is to recognize faces under variations in illumination. Previous works have indicated that the
variations in illumination can dramatically reduce the performance of face recognition. To this end，an efficient method
for face recognition which is robust under variable illumination is proposed in this paper. First of all, a discrete cosine
transform(DCT) in the logarithm domain is employed to preprocess the images, removing the illumination variations by
discarding an appropriate number of low-frequency DCT coefficients. Then, a face image is partitioned into several
patches, and we classify the patches using Sparse Representation-based Classification, respectively. At last, the identity
of a test image can be determined by the classification results of its patches. Experimental results on the Yale B database
and the CMU PIE database show that excellent recognition rates can be achieved by the proposed method.