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Sparsity augmented discriminative sparse representation for face recognition

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Abstract

Sparse representation-based classification (SRC) has acquired prominent capability in fields of machine learning and pattern recognition. Collaborative representation-based classification (CRC) has achieved a comparable recognition performance with higher speed compared to SRC, which has attracted much attention because it enables to forgo the computationally quite expensive l1-norm sparsity constraint. However, the traditional CRC method neglects the discriminability of representation and recent study has claimed that the sparsity should not be completely neglected for computational costs. In this paper, we propose a sparsity-augmented discriminative sparse representation-based classification method which considers the discriminability and sparsity of representation via augmenting an l2-norm regularization discriminative sparse representation with a computationally inexpensive sparse representation. We utilize an efficient classification method to achieve better performance with a comparable classification time. Experimental results on four face databases show the effectiveness of our proposed method.

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Notes

  1. Available at http://www.yongxu.org/code/SR%20survey-released.rar.

  2. Available at http://www.yongxu.org/code/TNN.zip.

  3. Available at http://www4.comp.polyu.edu.hk/~cslzhang/code/CRC.zip.

  4. Available at http://staffhome.ecm.uwa.edu.au/00053650/code.html.

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Acknowledgements

This research was supported by the 111 Project of Chinese Ministry of Education under Grant B12018, the National Nature Science Foundation of China [Grants U1836218, 61672265 and 61603159] and the Natural Science Foundation of Jiangsu Province of China under Grant BK20160293.

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Authors and Affiliations

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi, 214122, China

    Zhen Liu, Xiao-Jun Wu & Zhenqiu Shu

  2. Modern Educational Technology Center, Hubei Engineering University, Xiaogan, 432000, China

    Zhen Liu

  3. School of Computer Engineering, Jiangsu University of Technology, Changzhou, 231001, China

    Zhenqiu Shu

Authors
  1. Zhen Liu
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  2. Xiao-Jun Wu
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  3. Zhenqiu Shu
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Correspondence to Xiao-Jun Wu.

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Liu, Z., Wu, XJ. & Shu, Z. Sparsity augmented discriminative sparse representation for face recognition. Pattern Anal Applic 22, 1527–1535 (2019). https://doi.org/10.1007/s10044-019-00792-5

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