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Combination of linear regression classification and collaborative representation classification

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Abstract

Classification using the l 2-norm-based representation is usually computationally efficient and is able to obtain high accuracy in the recognition of faces. Among l 2-norm-based representation methods, linear regression classification (LRC) and collaborative representation classification (CRC) have been widely used. LRC and CRC produce residuals in very different ways, but they both use residuals to perform classification. Therefore, by combining the residuals of these two methods, better performance for face recognition can be achieved. In this paper, a simple weighted sum based fusion scheme is proposed to integrate LRC and CRC for more accurate recognition of faces. The rationale of the proposed method is analyzed. Face recognition experiments illustrate that the proposed method outperforms LRC and CRC.

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Acknowledgments

This paper is partially supported by National Natural Science Foundation of China under Grant Nos. 61300032, 61001037, 61271093 and 61102037. Thanks to Dr. Edward C. Mignot, Shandong University, for linguistic advice.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Hongzhi Zhang, Faqiang Wang, Dapeng Zhang & Kuanquan Wang

  2. College of Information and Computer Engineering, Northeast Forestry University, Harbin, China

    Yan Chen

  3. Harbin Vicog Intelligent Systems Co., Ltd, Harbin, China

    Jingdong Liu

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  1. Hongzhi Zhang
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  2. Faqiang Wang
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  3. Yan Chen
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  4. Dapeng Zhang
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  5. Kuanquan Wang
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  6. Jingdong Liu
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Correspondence to Hongzhi Zhang.

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Zhang, H., Wang, F., Chen, Y. et al. Combination of linear regression classification and collaborative representation classification. Neural Comput & Applic 25, 833–838 (2014). https://doi.org/10.1007/s00521-014-1564-6

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  • DOI: https://doi.org/10.1007/s00521-014-1564-6

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