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Robust collaborative representation-based classification via regularization of truncated total least squares

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Abstract

Collaborative representation-based classification has shown promising results on cognitive vision tasks like face recognition. It solves a linear problem with \(l_1\) or \(l_2\) norm regularization to obtain a stable sparse representation. Previous studies showed that the collaboration representation assisted the output of optimum sparsity constraint, but the choice of regularization also played a crucial role in stable representation. In this paper, we proposed a novel discriminative collaborative representation-based classification method via regularization implemented by truncated total least squares algorithm. The key idea of the proposed method is combining two coefficients obtained by \(l_2\) regularization and truncated TLS-based regularization. After evaluated by extensive experiments conducted on several benchmark facial databases, the proposed method is demonstrated to outperform the naive collaborative representation-based method, as well as some other state-of-the-art methods for face recognition. The regularization by truncation effectively and dramatically enhances sparsity constraint on coding coefficients in collaborative representation and increases robustness for face recognition.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (NSFC) (Grant Nos. 61602540, 61502208), Natural Science Foundation of Jiangsu Province of China (Grant No. BK20150522), Science and Technology Development Fund (FDCT) of Macao SAR (Grant No. 124/2014/A3), National Social Science Fund of China (Grant No. 15BTJ024), Fund Project of Humanities and Social Science Research of Chinese Ministry of Education (Grant No. 14YJAZH040), and Science and Technology Program of Huizhou City (Grant Nos. 2016X0422037, 2017C0405021).

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Authors and Affiliations

  1. Department of Computer and Information Science, University of Macau, Avenida da Universidade, Taipa, Macau

    Shaoning Zeng & Bob Zhang

  2. School of Information Science and Technology, Huizhou University, Huizhou, 516007, China

    Shaoning Zeng & Yuandong Lan

  3. College of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China

    Jianping Gou

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  1. Shaoning Zeng
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Correspondence to Bob Zhang.

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Zeng, S., Zhang, B., Lan, Y. et al. Robust collaborative representation-based classification via regularization of truncated total least squares. Neural Comput & Applic 31, 5689–5697 (2019). https://doi.org/10.1007/s00521-018-3403-7

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