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Fingerprint Pore Extraction Using U-Net Based Fully Convolutional Network

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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

The public demand for personal safety is increasing rapidly. Fingerprint features as the most commonly used bio-signature need to improve their safety continuously. The third level features of fingerprint (especially the sweat pores) can be added to the automatic fingerprint recognition system to increase the accuracy of fingerprint identification in a variety of environments. Due to perspiration activities, the shape and size sweat of pores are varying spatially and temporally. Extraction of fingerprint pores is both critical and challenging. In this paper, we adapt a novel fully convolutional neural network called U-net for ridges and sweat pores extraction. The PolyU High-Resolution-Fingerprint (HRF) database is used for testing of the proposed method. The results show the validity of the proposed method. With the majority of the pores correctly extracted, the proposed method can serve for fingerprint recognition using Level 3 features.

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Acknowledgments

This research is partially supported by Natural Science Foundation of China (61602414, 61402411).

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, People’s Republic of China

    Haixia Wang, Xicheng Yang, Lingtao Ma & Ronghua Liang

Authors
  1. Haixia Wang
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  2. Xicheng Yang
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  3. Lingtao Ma
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  4. Ronghua Liang
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Corresponding author

Correspondence to Ronghua Liang .

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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Wang, H., Yang, X., Ma, L., Liang, R. (2017). Fingerprint Pore Extraction Using U-Net Based Fully Convolutional Network. 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_30

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_30

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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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