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Fingerprint Synthesis

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Handbook of Fingerprint Recognition

Abstract

Synthetic fingerprints, when properly generated, represent a reasonable substitute for real fingerprints for the design, training, and benchmarking of fingerprint recognition algorithms. This approach is particularly useful to deal with emerging privacy regulations (e.g., EU-GDPR) limiting the use of personally identifiable information. This chapter introduces fingerprint synthesis and focuses on the two main categories of generation approaches: (i) first generate a master fingerprint and then derive multiple impressions (e.g., SFinGe); (ii) generative models (e.g., GAN) for the direct synthesis of fingerprint images. Validation of synthetic generators through large scale experiments is finally presented.

Invited Chapter by Raffaele Cappelli, University of Bologna.

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Notes

  1. 1.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Bologna, Cesena, Italy

    Davide Maltoni

  2. Department of Computer Science and Engineering, University of Bologna, Cesena, Italy

    Dario Maio

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Anil K. Jain

  4. Department of Automation, Tsinghua University, Beijing, China

    Jianjiang Feng

Authors
  1. Davide Maltoni
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  2. Dario Maio
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  3. Anil K. Jain
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  4. Jianjiang Feng
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Maltoni, D., Maio, D., Jain, A.K., Feng, J. (2022). Fingerprint Synthesis. In: Handbook of Fingerprint Recognition. Springer, Cham. https://doi.org/10.1007/978-3-030-83624-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-83624-5_7

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