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End-to-End Generative Adversarial Network for Palm-Vein Recognition

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Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

Abstract

Palm-vein recognition has received increasing researchers’ attention in recent years. However, palm-vein recognition faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this paper proposes an end-to-end convolutional neural network to automatically extract vein network features, thus without resorting to any hand-crafted features. Firstly, we label the palm-vein pixels based on several handcraft-based segmentation methods and reconstruct a training set accordingly. Secondly, an end-to-end vein segmentation model is proposed based on a generative adversarial network. After training, this model outputs a map where each value is the probability that the corresponding pixel belongs to a vein pattern. The resulting map is then subject to binarization by thresholding and stored in a binary image, used subsequently for verification matching. The experimental results on the public CASIA palm-vein dataset demonstrate the effectiveness of our proposed method.

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Acknowledgement

This work was supported by Institut Mines Telecom/Telecom SudParis, the National Natural Science Foundation of China (Grant No. 61976030, 61402063), the Natural Science Foundation Project of Chongqing (Grant No. cstc2017jcyjAX0002, Grant No. cstc2018jcyjAX0095, Grant No. cstc2017zdcy-zdyfX0067), and the Natural Science Foundation of Chongqing Municipal Education Commission (KJQN201900848).

Author information

Authors and Affiliations

  1. Chongqing Engineering Laboratory of Detection Control and Integrated System, Chongqing Technology and Business University, Chongqing, China

    Huafeng Qin

  2. Telecom SudParis, Institut Polytechnique de Paris, Courcouronnes, France

    Mounîm A. El Yacoubi

Authors
  1. Huafeng Qin
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  2. Mounîm A. El Yacoubi
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Corresponding author

Correspondence to Huafeng Qin .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

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Qin, H., El Yacoubi, M.A. (2020). End-to-End Generative Adversarial Network for Palm-Vein Recognition. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_62

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

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