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

  • Kiran B. RajaEmail author
  • R. Raghavendra
  • Sushma Venkatesh
  • Marta Gomez-Barrero
  • Christian Rathgeb
  • Christoph Busch
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (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.

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kiran B. Raja
    • 1
    Email author
  • R. Raghavendra
    • 1
  • Sushma Venkatesh
    • 1
  • Marta Gomez-Barrero
    • 2
  • Christian Rathgeb
    • 2
  • Christoph Busch
    • 1
  1. 1.Norwegian Biometrics LaboratoryNorwegian University of Science and Technology (NTNU)TrondheimNorway
  2. 2.da/sec Biometrics and Internet Security Research GroupHochschule DarmstadtDarmstadtGermany

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