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Journal of Electronic Imaging

Robust face recognition via gradient-based sparse representation
Author(s): Peng Ma; Dan Yang; Yongxin Ge; Xiaohong Zhang; Ying Qu; Sheng Huang; Jiwen Lu
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Paper Abstract

Although sparse representation (SR) based on the l1-norm and l2-norm have achieved promising classification results for face recognition (FR) from frontal views, they both require an overcomplete training dictionary, which is usually unrealistic. We focus on addressing the problem of performing FR with SR with an incomplete dictionary. Motivated by the fact that image gradients could explicitly consider the relationships between neighboring pixel points and be less sensitive to illumination than image pixels, we introduce image gradients to SR and propose gradient-based sparse representation classification (GSRC). By combining image pixels and image gradients, GSRC has less model error and requires fewer training samples from each individual than sparse representation–based classification (SRC). Furthermore, GSRC can easily be combined with dimensionality reduction algorithms and be solved by the regularized least-square method, which makes GSRC work much faster than SRC. Extensive experimental results demonstrate that GSRC is quite efficient for both incomplete dictionary and occlusion and has a reasonable speed.

Paper Details

Date Published: 1 February 2013
PDF: 14 pages
J. Electron. Imaging. 22(1) 013018 doi: 10.1117/1.JEI.22.1.013018
Published in: Journal of Electronic Imaging Volume 22, Issue 1
Show Author Affiliations
Peng Ma, Chongqing Univ. (China)
Dan Yang, Chongqing Univ. (China)
Yongxin Ge, Chongqing Univ. (China)
Xiaohong Zhang, Chongqing Univ. (China)
Ying Qu, Chongqing Univ. (China)
Sheng Huang, Chongqing Univ. (China)
Jiwen Lu


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