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Local Orientation Binary Pattern with Use for Palmprint Recognition

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Book cover Biometric Recognition (CCBR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10568))

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Abstract

In this paper, we extensively exploit the discriminative orientation features of palmprint, including the principal orientation and corresponding orientation confidence, and further propose a local orientation binary pattern (LOBP) for palmprint recognition. Different from the existing binary based representation methods, the LOBP method first captures the principal orientation consistency by comparing the center point with the neighbor sets, and then captures the confidence variations by thresholding the center confidence with neighborhoods so as to obtain orientation binary pattern (OBP) and confidence binary pattern (CBP), respectively. Furthermore, the block-wise statistics of OBP and CBP are concentrated to generate a novel descriptor, namely LOBP, of palmprint. Experiment results on different types of palmprint databases demonstrate the effectiveness of the proposed method.

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References

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Acknowledgment

This paper is partially supported by Guangdong Province high-level personnel of special support program (No. 2016TX03X164), and Shenzhen Fundamental Research fund (JCYJ20160331185006518).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

    Lunke Fei, Shaohua Teng, Wei Zhang & Xiaozhao Fang

  2. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Yong Xu

  3. School of Software, East China Jiaotong University, Nanchang, China

    Wenliang Tang

Authors
  1. Lunke Fei
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  2. Yong Xu
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  3. Shaohua Teng
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  4. Wei Zhang
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  5. Wenliang Tang
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  6. Xiaozhao Fang
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Corresponding author

Correspondence to Lunke Fei .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Computing and Technology, Chinese Academy of Sciences, Beijing, China

    Yong Xu

  5. Shenzhen University, Shenzhen, China

    Linlin Shen

  6. Tsinghua University, Beijing, China

    Jianjiang Feng

  7. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  8. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yu Qiao

  9. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China

    Zhenhua Guo

  10. Shenzhen University, Shenzhen, China

    Shiqi Yu

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Fei, L., Xu, Y., Teng, S., Zhang, W., Tang, W., Fang, X. (2017). Local Orientation Binary Pattern with Use for Palmprint Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69922-6

  • Online ISBN: 978-3-319-69923-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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