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Journal of Electronic Imaging

Two-stage sparse representation-based face recognition with reconstructed images
Author(s): Guangtao Cheng; Zhanjie Song; Yang Lei; Xiuning Han
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Paper Abstract

In order to address the challenges that both the training and testing images are contaminated by random pixels corruption, occlusion, and disguise, a robust face recognition algorithm based on two-stage sparse representation is proposed. Specifically, noises in the training images are first eliminated by low-rank matrix recovery. Then, by exploiting the first-stage sparse representation computed by solving a new extended 1-minimization problem, noises in the testing image can be successfully removed. After the elimination, feature extraction techniques that are more discriminative but are sensitive to noise can be effectively performed on the reconstructed clean images, and the final classification is accomplished by utilizing the second-stage sparse representation obtained by solving the reduced 1-minimization problem in a low-dimensional feature space. Extensive experiments are conducted on publicly available databases to verify the superiority and robustness of our algorithm.

Paper Details

Date Published: 15 October 2014
PDF: 11 pages
J. Electron. Imag. 23(5) 053021 doi: 10.1117/1.JEI.23.5.053021
Published in: Journal of Electronic Imaging Volume 23, Issue 5
Show Author Affiliations
Guangtao Cheng, Tianjin Univ. (China)
North China Institute of Aerospace Engineering (China)
Zhanjie Song, Tianjin Univ. (China)
Yang Lei, Tianjin Univ. (China)
Xiuning Han, Tianjin Univ. (China)

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