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An Introduction to Fingerprint Presentation Attack Detection Read chapter Read first chapter

2019 | OriginalPaper | Chapter

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

Authors : Kiran B. Raja, R. Raghavendra, Sushma Venkatesh, Marta Gomez-Barrero, Christian Rathgeb, Christoph Busch

Published in: Handbook of Biometric Anti-Spoofing

Publisher: Springer International Publishing

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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.

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previous chapter An Introduction to Fingerprint Presentation Attack Detection
next chapter Optical Coherence Tomography for Fingerprint Presentation Attack Detection
Footnotes
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Metadata
Title
A Study of Hand-Crafted and Naturally Learned Features for Fingerprint Presentation Attack Detection
Authors
Kiran B. Raja
R. Raghavendra
Sushma Venkatesh
Marta Gomez-Barrero
Christian Rathgeb
Christoph Busch
Copyright Year
2019
Book
Handbook of Biometric Anti-Spoofing

Print ISBN: 978-3-319-92626-1

Electronic ISBN: 978-3-319-92627-8

Copyright Year: 2019

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