Type of Publication: Article in Journal

Identifying Users by Their Hand Tracking Data in Augmented and Virtual Reality

Author(s):

Liebers, Jonathan; Brockel, Sascha; Gruenefeld, Uwe; Schneegass, Stefan

Title of Journal:

International Journal of Human–Computer Interaction

Publication Date:

2022

Digital Object Identifier (DOI):

doi:10.1080/10447318.2022.2120845

Fulltext:

Identifying Users by Their Hand Tracking Data in Augmented and Virtual Reality (8.07 MB)

Citation:

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Abstract

Nowadays, Augmented and Virtual Reality devices are widely available and are often shared among users due to their high cost. Thus, distinguishing users to offer personalized experiences is essential. However, currently used explicit user authentication(e.g., entering a password) is tedious and vulnerable to attack. Therefore, this work investigates the feasibility of implicitly identifying users by their hand tracking data. In particular, we identify users by their uni- and bimanual finger behavior gathered from their interaction with eight different universal interface elements, such as buttons and sliders. In two sessions, we recorded the tracking data of 16 participants while they interacted with various interface elements in Augmented and Virtual Reality. We found that user identification is possible with up to 95 % accuracy across sessions using an explainable machine learning approach. We conclude our work by discussing differences between interface elements, and feature importance to provide implications for behavioral biometric systems.