Knowledge-driven Biometric Authentication in Virtual Reality

Mathis, F., Fawaz, H. I. and Khamis, M. (2020) Knowledge-driven Biometric Authentication in Virtual Reality. In: ACM CHI Conference on Human Factors in Computing Systems (CHI' 20 Extended Abstracts), Honolulu, HI, USA, 25-30 April 2020, ISBN 9781450368193 (doi: 10.1145/3334480.3382799)

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With the increasing adoption of virtual reality (VR) in public spaces, protecting users from observation attacks is becoming essential to prevent attackers from accessing context-sensitive data or performing malicious payment transactions in VR. In this work, we propose RubikBiom, a knowledge-driven behavioural biometric authentication scheme for authentication in VR. We show that hand movement patterns performed during interactions with a knowledge-based authentication scheme (e.g., when entering a PIN) can be leveraged to establish an additional security layer. Based on a dataset gathered in a lab study with 23 participants, we show that knowledge-driven behavioural biometric authentication increases security in an unobtrusive way. We achieve an accuracy of up to 98.91% by applying a Fully Convolutional Network (FCN) on 32 authentications per subject. Our results pave the way for further investigations towards knowledge-driven behavioural biometric authentication in VR.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Mathis, Mr Florian and Khamis, Dr Mohamed
Authors: Mathis, F., Fawaz, H. I., and Khamis, M.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2020 by the author/owner(s).
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
309501RSE EnterpriseMohamed KhamisThe Royal Society of Edinburgh (ROYSOCED)65040Computing Science