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Relaxed 2-D Principal Component Analysis by Lp Norm for Face Recognition

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Intelligent Computing Theories and Application (ICIC 2019)

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Abstract

A relaxed two-dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-L1 and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal Lp-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.

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Acknowledgments

This paper is supported in part by National Natural Science Foundation of China under grant 11771188.

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Authors and Affiliations

  1. Jiangsu Normal University, Xuzhou, 221116, China

    Xiao Chen, Zhigang Jia & Meixiang Zhao

  2. Cognitive Computing Lab, Baidu Research, Beijing, 100193, China

    Yunfeng Cai

Authors
  1. Xiao Chen
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  2. Zhigang Jia
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  3. Yunfeng Cai
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  4. Meixiang Zhao
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Corresponding author

Correspondence to Zhigang Jia .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic University of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

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Chen, X., Jia, Z., Cai, Y., Zhao, M. (2019). Relaxed 2-D Principal Component Analysis by Lp Norm for Face Recognition. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_19

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  • DOI: https://doi.org/10.1007/978-3-030-26763-6_19

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