Exploring the Stability of Behavioral Biometrics in Virtual Reality in a Remote Field Study


Behavioral biometrics has recently become a viable alternative method for user identification in Virtual Reality (VR). Its ability to identify users based solely on their implicit interaction allows for high usability and removes the burden commonly associated with security mechanisms. However, little is known about the temporal stability of behavior (i.e., how behavior changes over time), as most previous works were evaluated in highly controlled lab environments over short periods. In this work, we present findings obtained from a remote field study (N = 15) that elicited data over a period of eight weeks from a popular VR game. We found that there are changes in people’s behavior over time, but that two-session identification still is possible with a mean F1-score of up to 71%, while an initial training yields 86%. However, we also see that performance can drop by up to over 50 percentage points when testing with later sessions, compared to the first session, particularly for smaller groups. Thus, our findings indicate that the use of behavioral biometrics in VR is convenient for the user and practical with regard to changing behavior and also reliable regarding behavioral variation.

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Jonathan Liebers, Christian Burschik, Uwe Gruenefeld, and Stefan Schneegass. 2023. Exploring the Stability of Behavioral Biometrics in Virtual Reality in a Remote Field Study: Towards Implicit and Continuous User Identification through Body Movements. In Proceedings of the 29th ACM Symposium on Virtual Reality Software and Technology (VRST '23). Association for Computing Machinery, New York, NY, USA, Article 30, 1–12. https://doi.org/10.1145/3611659.3615696