27 October 2013 Face recognition under variable illumination via sparse representation of patches
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Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 89190D (2013) https://doi.org/10.1117/12.2031388
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
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.
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Shouke Fan, Shouke Fan, Rui Liu, Rui Liu, Weiguo Feng, Weiguo Feng, Ming Zhu, Ming Zhu, } "Face recognition under variable illumination via sparse representation of patches", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 89190D (27 October 2013); doi: 10.1117/12.2031388; https://doi.org/10.1117/12.2031388
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