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Illumination robust face recognition using random projection and sparse representation

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Abstract

Under uneven illumination, the performances degrade significantly for some existing face recognition methods. It is a challenge for face recognition methods to work effectively under different illumination conditions. In this paper, an illumination robust face recognition method, based on random projection and sparse representation, is proposed. In the proposed method, face images are preliminary illumination normalized by gamma correction and difference of Gaussian filtering, and then several projection spaces are obtained by iterative random projection, followed by constructing an initial sample space using Fisher discrimination analysis. This scheme enriches the discrimination abilities of sample features and achieves the security and completeness for biometric template. Test samples are sparsely decomposed into each subspace, and based on statistical average residual, a modified sparse representation method is proposed to realize face recognition with higher stability and illumination robustness. Experimental results indicate that the proposed method provides competitive performance with acceptable computational efficiency. Specifically, for the five subsets of Yale B database, our approach achieves 99.74% average recognition rate, which performs higher accuracy than that of comparative methods.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China under Grants 61271399, 61471212; Natural Science Foundation of Zhejiang Province under Grants LY16F010001; Natural Science Foundation of Ningbo under Grants 2016A610091; and K.C.Wong Magna Fund in Ningbo University.

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, China

    Wei Jin, Fei Gong & Randi Fu

  2. Intelligent Household Appliances Engineering Center, Zhejiang Business Technology Institute, Ningbo, 315012, China

    Xingbin Zeng

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  1. Wei Jin
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  2. Fei Gong
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  3. Xingbin Zeng
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  4. Randi Fu
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Correspondence to Wei Jin.

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Jin, W., Gong, F., Zeng, X. et al. Illumination robust face recognition using random projection and sparse representation. SIViP 12, 721–729 (2018). https://doi.org/10.1007/s11760-017-1213-5

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  • DOI: https://doi.org/10.1007/s11760-017-1213-5

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