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Inpainting and Denoising Challenges pp 51–61Cite as

FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks

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Part of the book series: The Springer Series on Challenges in Machine Learning ((SSCML))

Abstract

Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extraction of the fingerprint from a degraded image requires denoising and inpainting. We propose to address these problems with an end-to-end trainable Convolutional Neural Network based architecture called FPD-M-net, by posing the fingerprint denoising and inpainting problem as a segmentation (foreground) task. Our architecture is based on the M-net with a change: structure similarity loss function, used for better extraction of the fingerprint from the noisy background. Our method outperforms the baseline method and achieves an overall 3rd rank in the Chalearn LAP Inpainting Competition Track 3—Fingerprint Denoising and Inpainting, ECCV 2018.

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Notes

  1. 1.

    http://chalearnlap.cvc.uab.es/challenge/26/track/32/description/.

  2. 2.

    http://chalearnlap.cvc.uab.es/dataset/32/description/.

  3. 3.

    https://github.com/adigasu/FDPMNet.

  4. 4.

    http://chalearnlap.cvc.uab.es/challenge/26/track/32/baseline/.

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Authors and Affiliations

  1. Center for Visual Information Technology (CVIT), IIIT, Hyderabad, India

    Sukesh Adiga V & Jayanthi Sivaswamy

Authors
  1. Sukesh Adiga V
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  2. Jayanthi Sivaswamy
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Corresponding author

Correspondence to Sukesh Adiga V .

Editor information

Editors and Affiliations

  1. Department of Mathematics & Informatics, Universitat de Barcelona, Computer Vision Center, Barcelona, Spain

    Sergio Escalera

  2. Aix-Marseille University, Marseille, France

    Stephane Ayache

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jun Wan

  4. Computer Vision Center, Autonomous University of Barcelona, Bellaterra, Barcelona, Spain

    Meysam Madadi

  5. Radboud University Nijmegen, Nijmegen, The Netherlands

    Umut Güçlü

  6. Open University of Catalonia, Barcelona, Spain

    Xavier Baró

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Adiga V, S., Sivaswamy, J. (2019). FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-net Based Convolutional Neural Networks. In: Escalera, S., Ayache, S., Wan, J., Madadi, M., Güçlü, U., Baró, X. (eds) Inpainting and Denoising Challenges. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-25614-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-25614-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25613-5

  • Online ISBN: 978-3-030-25614-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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