15 October 2014 Two-stage sparse representation-based face recognition with reconstructed images
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J. of Electronic Imaging, 23(5), 053021 (2014). doi:10.1117/1.JEI.23.5.053021
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.
© 2014 SPIE and IS&T
Guangtao Cheng, Zhanjie Song, Yang Lei, Xiuning Han, "Two-stage sparse representation-based face recognition with reconstructed images," Journal of Electronic Imaging 23(5), 053021 (15 October 2014). http://dx.doi.org/10.1117/1.JEI.23.5.053021
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KEYWORDS
Facial recognition systems

Feature extraction

Principal component analysis

Databases

Detection and tracking algorithms

Image classification

Associative arrays

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