International Journal of Intelligent Information Systems

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Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning

Received: 24 May 2018    Accepted: 30 August 2018    Published: 26 September 2018
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Abstract

In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.

DOI 10.11648/j.ijiis.20180702.11
Published in International Journal of Intelligent Information Systems (Volume 7, Issue 2, April 2018)
Page(s) 15-22
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Pattern Recognition, Dictionary Learning, Kernel Space, Collaborative Representation

References
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Author Information
  • Department of Electronic Information and Engineering, Shandong Huayu University of Technology, Dezhou, China; Department of Information and Engineering, Yanshan University, Qinhuangdao, China

  • Department of Information and Engineering, Yanshan University, Qinhuangdao, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, China

  • Department of Information and Engineering, Yanshan University, Qinhuangdao, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, China

  • Department of Information and Engineering, Yanshan University, Qinhuangdao, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, China

  • Department of Information and Engineering, Yanshan University, Qinhuangdao, China; Hebei Key Laboratory of Information Transmission and Signal Processing, Qinhuangdao, China

  • Department of Electronic Information and Engineering, Shandong Huayu University of Technology, Dezhou, China

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    Zheng-ping Hu, Yi Liu, Xuan Zhang, Yang-hua Yin, Rui-xue Zhang, et al. (2018). Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. International Journal of Intelligent Information Systems, 7(2), 15-22. https://doi.org/10.11648/j.ijiis.20180702.11

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    ACS Style

    Zheng-ping Hu; Yi Liu; Xuan Zhang; Yang-hua Yin; Rui-xue Zhang, et al. Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. Int. J. Intell. Inf. Syst. 2018, 7(2), 15-22. doi: 10.11648/j.ijiis.20180702.11

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    AMA Style

    Zheng-ping Hu, Yi Liu, Xuan Zhang, Yang-hua Yin, Rui-xue Zhang, et al. Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning. Int J Intell Inf Syst. 2018;7(2):15-22. doi: 10.11648/j.ijiis.20180702.11

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  • @article{10.11648/j.ijiis.20180702.11,
      author = {Zheng-ping Hu and Yi Liu and Xuan Zhang and Yang-hua Yin and Rui-xue Zhang and De-gang Sun},
      title = {Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning},
      journal = {International Journal of Intelligent Information Systems},
      volume = {7},
      number = {2},
      pages = {15-22},
      doi = {10.11648/j.ijiis.20180702.11},
      url = {https://doi.org/10.11648/j.ijiis.20180702.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijiis.20180702.11},
      abstract = {In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Kernel Collaborative Representation Classification Based on Adaptive Dictionary Learning
    AU  - Zheng-ping Hu
    AU  - Yi Liu
    AU  - Xuan Zhang
    AU  - Yang-hua Yin
    AU  - Rui-xue Zhang
    AU  - De-gang Sun
    Y1  - 2018/09/26
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijiis.20180702.11
    DO  - 10.11648/j.ijiis.20180702.11
    T2  - International Journal of Intelligent Information Systems
    JF  - International Journal of Intelligent Information Systems
    JO  - International Journal of Intelligent Information Systems
    SP  - 15
    EP  - 22
    PB  - Science Publishing Group
    SN  - 2328-7683
    UR  - https://doi.org/10.11648/j.ijiis.20180702.11
    AB  - In recent years, with the progress of technology, face recognition is used more and more widely in various fields. The classification algorithm based on sparse representation has made a great breakthrough in face recognition. However, face images are often affected by different poses, lighting, and expression changes, so test samples are often difficult to represent with limited original training samples. Due to the conventional dictionary learning methods lacking adaptability, we propose a kernel collaborative representation classification based on adaptive dictionary learning. In this paper, the coarse to fine sparse representation is related to the adaptive dictionary learning problem. First, the labeled atom dictionary is learned from each kind of training samples by sparse approximation. Based on this assumption, we use an efficient algorithm to generate an adaptive dictionary that is related with the test sample. Then, based on the adaptive class dictionary, the kernel collaborative representation is used to realize the inter class competition classification. The kernel function is combined with the coarse to fine sparse representation to extract the non-linear factors such as facial expression change, posture, illumination, occlusion and so on. The kernel collaborative representation is used to realize the inter class competition classification. The main advantage of this approach is to combine coarse to fine kernel collaborative representation with dictionary learning to generate adaptive dictionaries that approximate to the test image. Experimental results demonstrate that the proposed appraoch outperforms some previous state-of-the-art dictionary learning methods and sparse coding methods in face recognition.
    VL  - 7
    IS  - 2
    ER  - 

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