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A Study of Hand-Crafted and Naturally Learned Features for Fingerprint Presentation Attack Detection

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Handbook of Biometric Anti-Spoofing

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Fingerprint-based biometric systems have shown reliability in terms of accuracy in both biometric and forensic scenarios. Although fingerprint systems are easy to use, they are susceptible to presentation attacks that can be carried out by employing lifted or latent fingerprints. This work presents a systematic study of the fingerprint presentation attack detection (PAD aka., spoofing detection) using textural features. To this end, this chapter reports an evaluation of both hand-crafted features and naturally learned features via deep learning techniques for fingerprint presentation attack detection. The evaluation is presented on publicly available fake fingerprint database that consists of both bona fide (i.e., real) and presentation attack fingerprint samples captured by capacitive, optical and thermal sensors. The results indicate the need for further approaches that can detect attacks across data from different sensors.

K. B. Raja and R. Raghavendra—Equal contribution of authors.

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Notes

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    https://atvs.ii.uam.es/atvs/databases.jsp.

  2. 2.

    https://atvs.ii.uam.es/atvs/databases.jsp.

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Acknowledgements

This work was carried out under the funding for SWAN project from the Research Council of Norway under Grant No. IKTPLUSS-248030/O70. This work was partially supported by the German Federal Ministry of Education and Research (BMBF) as well as by the Hessen State Ministry for Higher Education, Research and the Arts (HMWK) within the Center for Research in Security and Privacy (CRISP, www.crisp-da.de).

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Authors and Affiliations

  1. Norwegian Biometrics Laboratory, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

    Kiran B. Raja, R. Raghavendra, Sushma Venkatesh & Christoph Busch

  2. da/sec Biometrics and Internet Security Research Group, Hochschule Darmstadt, Darmstadt, Germany

    Marta Gomez-Barrero & Christian Rathgeb

Authors
  1. Kiran B. Raja
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  2. R. Raghavendra
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  3. Sushma Venkatesh
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  4. Marta Gomez-Barrero
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  5. Christian Rathgeb
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  6. Christoph Busch
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Corresponding author

Correspondence to Kiran B. Raja .

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Editors and Affiliations

  1. Idiap Research Institute, Martigny, Switzerland

    Sébastien Marcel

  2. University of Southampton, Southampton, UK

    Mark S. Nixon

  3. Universidad Autonoma de Madrid, Madrid, Spain

    Julian Fierrez

  4. EURECOM, Biot Sophia Antipolis, France

    Nicholas Evans

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Raja, K.B., Raghavendra, R., Venkatesh, S., Gomez-Barrero, M., Rathgeb, C., Busch, C. (2019). A Study of Hand-Crafted and Naturally Learned Features for Fingerprint Presentation Attack Detection. In: Marcel, S., Nixon, M., Fierrez, J., Evans, N. (eds) Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-92627-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-92627-8_2

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  • Print ISBN: 978-3-319-92626-1

  • Online ISBN: 978-3-319-92627-8

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