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An Incremental Fast Policy Search Using a Single Sample Path Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Biometric Counter-Spoofing for Mobile Devices Using Gaze Information

Authors : Asad Ali, Nawal Alsufyani, Sanaul Hoque, Farzin Deravi

Published in: Pattern Recognition and Machine Intelligence

Publisher: Springer International Publishing

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Abstract

With the rise in the use of biometric authentication on mobile devices, it is important to address the security vulnerability of spoofing attacks where an attacker using an artefact representing the biometric features of a genuine user attempts to subvert the system. In this paper, techniques for presentation attack detection are presented using gaze information with a focus on their applicability for use on mobile devices. Novel features that rely on directing the gaze of the user and establishing its behaviour are explored for detecting spoofing attempts. The attack scenarios considered in this work include the use of projected photos, 2D and 3D masks. The proposed features and the systems based on them were extensively evaluated using data captured from volunteers performing genuine and spoofing attempts. The results of the evaluations indicate that gaze-based features have the potential for discriminating between genuine attempts and imposter attacks on mobile devices.

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Metadata
Title
Biometric Counter-Spoofing for Mobile Devices Using Gaze Information
Authors
Asad Ali
Nawal Alsufyani
Sanaul Hoque
Farzin Deravi
Copyright Year
2017
Book
Pattern Recognition and Machine Intelligence

Print ISBN: 978-3-319-69899-1

Electronic ISBN: 978-3-319-69900-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69900-4_2

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