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Chinese Conference on Biometric Recognition

CCBR 2017: Biometric Recognition pp 334–343Cite as

Feature Guided Fingerprint Pore Matching

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

Abstract

The huge number of sweat pores in fingerprint images results in low efficiency of direct pore (DP) matching methods. To overcome this drawback, this paper proposes a feature guided fingerprint pore matching method. It selects “distinctive” pores around the minutiae and singular points from fingerprint images which extremely reduced the number of pore features for matching. And then, the selected “distinctive” pores are matched using the-state-of-the-art DP matching methods. We also consider to take the select “distinctive” pores together with the extracted minutiae and singular points as a whole feature set for matching. The experimental results have shown that the matching time of the proposed method can be reduced to a quarter of the original time when the recognition accuracy is kept at the same level. Both of the matching time and recognition accuracy are improved when multi-features are taken as a whole set for matching.

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Acknowledgments

The work is supported by the NSFC funds 61403257 and Shenzhen Fundamental Research funds JCYJ20150324140036868.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Feng Liu, Yuanhao Zhao & Linlin Shen

Authors
  1. Feng Liu
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  2. Yuanhao Zhao
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  3. Linlin Shen
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Corresponding author

Correspondence to Feng Liu .

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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© 2017 Springer International Publishing AG

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Cite this paper

Liu, F., Zhao, Y., Shen, L. (2017). Feature Guided Fingerprint Pore Matching. 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_36

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_36

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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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