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Fingerprint Segmentation via Convolutional Neural Networks

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Biometric Recognition (CCBR 2017)

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

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Abstract

In automatic fingerprint identification systems, it is crucial to segment the fingerprint images. Inspired by the superiority of convolutional neural networks for various classification and regression tasks, we approach fingerprint segmentation as a binary classification problem and propose a convolutional neural network based method for fingerprint segmentation. Given a fingerprint image, we first apply the total variation model to decompose it into cartoon and texture components. Then, the obtained texture component image is divided into overlapping patches, which are classified by the trained convolutional neural network as either foreground or background. Based on the classification results and by applying morphology-based post-processing, we get the final segmentation result for the whole fingerprint image. In the experiments, we investigate the effect of different patch sizes on the segmentation performance, and compare the proposed method with state-of-the-art algorithms on FVC2000, FVC2002 and FVC2004. Experimental results demonstrate that the proposed method outperforms existing algorithms.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61202161), and Shenzhen Fundamental Research funds JCYJ20150324140036868 (No. 61403257).

Author information

Authors and Affiliations

  1. National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China

    Xiaowei Dai, Jie Liang & Qijun Zhao

  2. School of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Feng Liu

Authors
  1. Xiaowei Dai
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  2. Jie Liang
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  3. Qijun Zhao
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  4. Feng Liu
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Corresponding author

Correspondence to Qijun Zhao .

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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Dai, X., Liang, J., Zhao, Q., Liu, F. (2017). Fingerprint Segmentation via Convolutional Neural Networks. 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_35

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  • DOI: https://doi.org/10.1007/978-3-319-69923-3_35

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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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