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Two-Dimensional Color Uncorrelated Principal Component Analysis for Feature Extraction with Application to Face Recognition

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Biometric Recognition (CCBR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8232))

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Abstract

This paper proposes a two-dimensional color uncorrelated principal component analysis algorithm(2DCUPCA) for unsupervised subspace learning directly from color face images. The 2DCUPCA can be used to explore uncorrelated properties among color-based features, which contain minimum redundancy and ensure linear independence among features. Furthermore, the proposed 2DCUPCA provided the theoretical foundations analysis and proved the uncorrelated property between color-based features in theory. This makes it sure that the extracted features directly from three color image matrices will be uncorrelated. Finally, experimental results on the AR and FRGC-2 color face databases show that 2DCUPCA achieves better recognition performance than other color face recognition methods.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China

    Cairong Zhao & Duoqian Miao

  2. The Key Laboratory of “Embedded System and Service Computing”, Ministry of Education, Shanghai, 7201804, China

    Cairong Zhao & Duoqian Miao

  3. The Key Laboratory of “ Intelligent Perception and Systems for High-Dimensional Information ” (NJUST), Ministry of Education, Nanjing, 210094, China

    Cairong Zhao

Authors
  1. Cairong Zhao
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  2. Duoqian Miao
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Editor information

Editors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, 100190, Beijing, P.R. China

    Zhenan Sun

  2. Institute of Computing Technology, Chinese Academy of Sciences, 100190, Beijing, China

    Shiguan Shan

  3. School of Computer Science and Technology, Shandong University, 250101, Jinan, China

    Gongping Yang  & YiLong Yin  & 

  4. Software Engineering Institute, Tsinghua University, 100084, Beijing, China

    Jie Zhou

  5. School of Software, Beihang University, 100083, Beijing, China

    Yunhong Wang

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© 2013 Springer International Publishing Switzerland

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Zhao, C., Miao, D. (2013). Two-Dimensional Color Uncorrelated Principal Component Analysis for Feature Extraction with Application to Face Recognition. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_17

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  • DOI: https://doi.org/10.1007/978-3-319-02961-0_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02960-3

  • Online ISBN: 978-3-319-02961-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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