EyeSeeIdentity: Exploring Natural Gaze Behavior for Implicit User Identification during Photo Viewing

Abdrabou, Y., Hassib, M., Hu, S., Pfeuffer, K., Khamis, M. , Bulling, A. and Alt, F. (2024) EyeSeeIdentity: Exploring Natural Gaze Behavior for Implicit User Identification during Photo Viewing. In: Symposium on Usable Security and Privacy (USEC) 2024, San Diego, California, USA, 26 Feb - 01 Mar 2024, ISBN 9798989437252 (doi: 10.14722/usec.2024.23057)

Abstract

Existing gaze-based methods for user identification either require special-purpose visual stimuli or artificial gaze behaviour. Here, we explore how users can be differentiated by analysing natural gaze behaviour while freely looking at images. Our approach is based on the observation that looking at different images, for example, a picture from your last holiday, induces stronger emotional responses that are reflected in gaze behavioor and, hence, is unique to the person having experienced that situation. We collected gaze data in a remote study (N = 39) where participants looked at three image categories: personal images, other people’s images, and random images from the Internet. We demonstrate the potential of identifying different people using machine learning with an accuracy of 85%. The results pave the way towards a new class of authentication methods solely based on natural human gaze behaviour.

• Item Type: Conference Proceedings
• Additional Information: This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant No. 101021229, GEMINI: Gaze and Eye Movement in Interaction). It was also funded by dtec.bw – Digitalization and Technology Research Center of the Bundeswehr (Voice of Wisdom). dtec.bw is funded by the European Union – NextGenerationEU. Finally, A. Bulling was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No 801708.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Khamis, Dr Mohamed
• Authors: Abdrabou, Y., Hassib, M., Hu, S., Pfeuffer, K., Khamis, M., Bulling, A., and Alt, F.
• College/School: College of Science and Engineering > School of Computing Science
• ISBN: 9798989437252
• Copyright Holders: Copyright © 2024 The Authors
• Publisher Policy: Reproduced with the permission of the authors

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