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Fingerprint Indexing with Minutiae-Aided Fingerprint Multiscale Representation

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Pattern Recognition (ACPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14406))

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Abstract

The huge fingerprint database limits the speed of large-scale fingerprint identification, which can be alleviated by fingerprint indexing. The previous fixed-length representation used for fingerprint indexing achieves unsatisfactory performance due to no good use of fingerprint domain knowledge. Inspired by some attempts to introduce minutiae information, in this paper, we propose a tailored framework to extract the minutiae-aided fingerprint multiscale representation for fingerprint indexing. We design Minutiae Distribution and Directions Map (MDDM) to encode the information contained in minutiae. Inputting into the network along with the fingerprint images, the MDDM guide the network to pay more attention to the minutiae information. Considering the deep features with high resolution contain more small object information, i.e., the minutiae, we introduce the tailored Multiscale Feature Fusion (MFF) module to better use the semantic information in different layers. Extensive experiments show that the proposed fixed-length representation achieves better indexing performance on all benchmarks.

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), School of Intelligence Science and Technology, Peking University, Beijing, 100871, People’s Republic of China

    Song Wu, Zheng Wang, Zexi Jia, Chuanwei Huang, Hongyan Fei & Jufu Feng

Authors
  1. Song Wu
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  2. Zheng Wang
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  3. Zexi Jia
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  4. Chuanwei Huang
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  5. Hongyan Fei
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  6. Jufu Feng
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Corresponding author

Correspondence to Jufu Feng .

Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

    Huimin Lu

  2. The University of Sydney, Sydney, NSW, Australia

    Michael Blumenstein

  3. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. Osaka University, Osaka, Ibaraki, Japan

    Yasushi Yagi

  6. Kyushu Institute of Technology, Kitakyushu, Japan

    Tohru Kamiya

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Wu, S., Wang, Z., Jia, Z., Huang, C., Fei, H., Feng, J. (2023). Fingerprint Indexing with Minutiae-Aided Fingerprint Multiscale Representation. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14406. Springer, Cham. https://doi.org/10.1007/978-3-031-47634-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-47634-1_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47633-4

  • Online ISBN: 978-3-031-47634-1

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