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BFNet: A Lightweight Barefootprint Recognition Network

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Biometric Recognition (CCBR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14463))

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Abstract

In recent years, barefootprint-based biometrics has emerged as a novel research area. Compared with other biometrics, barefootprints are more covert and secure. However, due to the absence of large-scale datasets and the limited training data, it is difficult to achieve high accuracy for barefootprint recognition. In this paper, a barefootprint dataset named BFD is first proposed containing 54118 images from 3000 individuals of different genders, ages and weights. A novel barefootprint recognition network named BFNet is secondly proposed, which is enhanced by adding SENet, adjusting the width and depth of the network, and using an improved triplet loss function. Experiments show that BFNet achieves an accuracy of 94.0% and 98.3% respectively in Top-1 and Top-10 for the barefootprint identification task. BFNet achieves 98.9% of Area Under Curve (AUC) for the barefootprint verification task, with the False Acceptance Rate (FAR) of 0.00106 and the Equal Error Rate (EER) of 0.054.

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Acknowledgments

This work is supported by Double First-Class Innovation Research Project for People’s Public Security University of China (No. 2023SYL06).

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Authors and Affiliations

  1. School of Criminal Investigation, People’s Public Security University of China, Beijing, 10038, China

    Yi Yang & Yunqi Tang

  2. Dalian Everspry Sci&Tech Co., Ltd., Dalian, 116085, China

    Junjian Cui & Xiaorui Zhao

Authors
  1. Yi Yang
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  2. Yunqi Tang
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  3. Junjian Cui
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  4. Xiaorui Zhao
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Corresponding author

Correspondence to Yunqi Tang .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Wei Jia

  2. South China University of Technology, Guangzhou, China

    Wenxiong Kang

  3. China University of Mining and Technology, Xuzhou, China

    Zaiyu Pan

  4. Shandong University, Jinan, China

    Xianye Ben

  5. China University of Mining and Technology, Xuzhou, China

    Zhengfu Bian

  6. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  7. Chinese Academy of Sciences, Beijing, China

    Zhaofeng He

  8. China University of Mining and Technology, Xuzhou, China

    Jun Wang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Yang, Y., Tang, Y., Cui, J., Zhao, X. (2023). BFNet: A Lightweight Barefootprint Recognition Network. In: Jia, W., et al. Biometric Recognition. CCBR 2023. Lecture Notes in Computer Science, vol 14463. Springer, Singapore. https://doi.org/10.1007/978-981-99-8565-4_29

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  • DOI: https://doi.org/10.1007/978-981-99-8565-4_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8564-7

  • Online ISBN: 978-981-99-8565-4

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