Robust face recognition via gradient-based sparse representation

Peng Ma; Dan Yang; Yongxin Ge; Xiaohong Zhang; Ying Qu; Sheng Huang; Jiwen Lu

doi:10.1117/1.JEI.22.1.013018

1 February 2013 Robust face recognition via gradient-based sparse representation

Peng Ma, Dan Yang, Yongxin Ge, Xiaohong Zhang, Ying Qu, Sheng Huang, Jiwen Lu

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 1, 013018 (February 2013). https://doi.org/10.1117/1.JEI.22.1.013018

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.

Citation Download Citation

Peng Ma, Dan Yang, Yongxin Ge, Xiaohong Zhang, Ying Qu, Sheng Huang, and Jiwen Lu "Robust face recognition via gradient-based sparse representation," Journal of Electronic Imaging 22(1), 013018 (1 February 2013). https://doi.org/10.1117/1.JEI.22.1.013018

Published: 1 February 2013

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