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Chinese Conference on Biometric Recognition

CCBR 2017: Biometric Recognition pp 411–419Cite as

Coarse-to-Fine Iris Recognition Based on Multi-variant Ordinal Measures Feature Complementarity

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

Abstract

Iris recognition inevitably need to tackle extremely large scale database matching issue which challenges the iris recognition in both computing efficiency and accuracy. As a feasible solution, the iris image classification has great potential and needs further studies. We propose a multi-variant Ordinal Measures feature complementarity based coarse-to-fine iris recognition strategy. Two OM variant feature are proposed for iris classification. One is very large scale OM feature (VLSOM), and the other is histogram statistics of OM Run-Length Coding (HOMRLC). VLSOM, HOMRLC and OM describes overall appearance, global statistic and local characteristics of iris respectively. Extensive experiments show advantages of the proposed complementarity feature.

H. Zhang—This work is supported by the Natural Science Foundation of China (61503365) (61603385).

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Author information

Authors and Affiliations

  1. Institute of Software, CAS, Beijing, China

    Hui Zhang & Rui Wang

  2. Institute of Automation, CAS, Beijing, China

    Man Zhang

  3. Beijing IrisKing Co., Ltd., Beijing, China

    Zhaofeng He

  4. Minzu University of China, Beijing, China

    Hang Zou

Authors
  1. Hui Zhang
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  2. Man Zhang
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  3. Zhaofeng He
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  4. Hang Zou
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  5. Rui Wang
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Corresponding author

Correspondence to Hui Zhang .

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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Zhang, H., Zhang, M., He, Z., Zou, H., Wang, R. (2017). Coarse-to-Fine Iris Recognition Based on Multi-variant Ordinal Measures Feature Complementarity. 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_44

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_44

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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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