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Penalized collaborative representation based classification for face recognition

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Abstract

The collaborative representation classification (CRC) exhibits superiority in both accuracy and computational efficiency. However, when representing the test sample by a linear combination of the training samples, the CRC does not account for the following: the probability of the test sample being from the same class as the training sample far from it is small. In this paper, we propose the algorithm, Penalized Collaborative Representation (PCR), which first uses the original collaborative representation to compute the distance between each training and test sample, and then treats these distances as penalized coefficients to design the penalized collaborative representation. The experimental results on multiple face databases show that our classifier, designed according PCR, has a very satisfactory classification performance.

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Acknowledgments

This work is partially supported by National Natural Science Foundation of China under Grant Nos. 61373063, 61233011, 61125305, 61375007, 61220301, and by National Basic Research Program of China under Grant No. 2014CB349303, and supported by the 2013 Higher School Discipline and Specialty Construction Project in Guangdong Province (2013LYM 0055 and 2013KJCX0127).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

    Wei Huang & Zhong Jin

  2. Department of Computer Science and Engineering, Hanshan Normal University, Chaozhou, China

    Wei Huang & Xiaohui Wang

  3. Department of Mathematics and Statics, Hanshan Normal University, Chaozhou, China

    Jianzhong Li

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  1. Wei Huang
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  2. Xiaohui Wang
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  3. Zhong Jin
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  4. Jianzhong Li
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Correspondence to Wei Huang.

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Huang, W., Wang, X., Jin, Z. et al. Penalized collaborative representation based classification for face recognition. Appl Intell 43, 722–731 (2015). https://doi.org/10.1007/s10489-015-0672-z

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  • DOI: https://doi.org/10.1007/s10489-015-0672-z

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