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Multi-resolution dictionary collaborative representation for face recognition

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Abstract

In this paper, a multi-resolution dictionary collaborative representation(MRDCR) method for face recognition is proposed. Unlike most of the traditional sparse learning methods, such as sparse representation-based classification(SRC) methods and dictionary learning(DL)-based methods, which concentrate only on a single resolution, we consider the fact that the resolutions of real-world face images are variable. We use multiple dictionaries each being related with a resolution to collaboratively represent the test image. Main advantages of this work are summarized as follows. First, we extend the traditional collaborative representation-based classification(CRC) method to the multi-resolution dictionary case, which obtains better recognition accuracy than traditional SRC/CRC methods. Second, comparing with conventional DL methods and recently proposed multi-resolution dictionary learning(MRDL) method, MRDCR still shows superior performance, even in the case of random baboon block occlusion. Third, on the small-scale face databases, our method has achieved better results than some deep learning methods. Last, MRDCR has a closed-form solution, which makes it more efficient than most of the traditional sparse learning methods. The experimental results on five benchmark face databases and a Virus database demonstrate that our proposed MRDCR method outperforms many state-of-the-art dictionary learning and sparse representation methods. The MATLAB code will be available at https://github.com/masterliuhzen/.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62020106012, U1836218, 61672265, 62162033), the 111 Project of Ministry of Education of China (B12018), and the Natural Science Foundation of Xiaogan, China (XGKJ2020010063).

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Authors and Affiliations

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China

    Zhen Liu & Xiao-Jun Wu

  2. School of Computer and Information Science, Hubei Engineering University, Xiaogan, 432000, Hubei Province, China

    Zhen Liu

  3. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Zhenqiu Shu

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  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. Multi-resolution dictionary collaborative representation for face recognition. Pattern Anal Applic 24, 1793–1803 (2021). https://doi.org/10.1007/s10044-021-00987-9

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