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Biometric Presentation Attack Detection for Mobile Devices Using Gaze Information

Alsufyani, Nawal (2018) Biometric Presentation Attack Detection for Mobile Devices Using Gaze Information. Doctor of Philosophy (PhD) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:72948)

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Language: English

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Abstract

Facial recognition systems are among the most widely deployed in biometric applications. However, such systems are vulnerable to presentation attacks (spoofing), where a person tries to disguise as someone else by mimicking their biometric data and thereby gaining access to the system. Significant research attention has been directed toward developing robust strategies for detecting such attacks and thus assuring the security of these systems in real-world applications. This thesis is focused on presentation attack detection for face recognition systems using a gaze tracking approach.

A number of novel gaze-based features were explored to develop the presentation attack detection algorithm. Initial experiments using the KGDD provided an encouraging indication of the potential of the proposed system for attack detection. In order to explore the feasibility of the scheme on a real hand held device, another database, the Mobile KGDD (MKGDD), was collected from 30 participants using a single mobile device (Google Nexus 6), to test the proposed features.

Comprehensive experimental analysis has been performed on the two collected databases for each of the proposed features. Performance evaluation results indicate that the proposed gaze-based features are effective in discriminating between genuine and presentation attack attempts.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Deravi, Farzin
Thesis advisor: Hoque, Sanaul
Uncontrolled keywords: Presentation attack detection, Database
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 28 Mar 2019 10:56 UTC
Last Modified: 01 Aug 2019 10:44 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72948 (The current URI for this page, for reference purposes)
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