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Exploring the Stability of Behavioral Biometrics in Virtual Reality in a Remote Field Study: Towards Implicit and Continuous User Identification through Body Movements

Authors:

Jonathan Liebers,

Christian Burschik,

Uwe Gruenefeld,

Stefan SchneegassAuthors Info & Claims

VRST '23: Proceedings of the 29th ACM Symposium on Virtual Reality Software and Technology

Article No.: 30, Pages 1 - 12

https://doi.org/10.1145/3611659.3615696

Published: 09 October 2023 Publication History

Abstract

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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Cited By

Liebers JLaskowski PRademaker FSabel LHoppen JGruenefeld USchneegass S(2024)Kinetic Signatures: A Systematic Investigation of Movement-Based User Identification in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642471(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642471
Rack CNair VSchach LFoschum FRoth MLatoschik M(2024)Navigating the Kinematic Maze: Analyzing, Standardizing and Unifying XR Motion Datasets2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00098(507-514)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00098
Li MZafar NBanerjee NBanerjee S(2024)Evaluating Deep Networks for Detecting User Familiarity with VR from Hand Interactions2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)10.1109/AIxVR59861.2024.00036(226-230)Online publication date: 17-Jan-2024
https://doi.org/10.1109/AIxVR59861.2024.00036

Index Terms

Exploring the Stability of Behavioral Biometrics in Virtual Reality in a Remote Field Study: Towards Implicit and Continuous User Identification through Body Movements
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. Field studies
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Usability in security and privacy
  2. Security services
    1. Authentication
      1. Biometrics

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cover image ACM Conferences

VRST '23: Proceedings of the 29th ACM Symposium on Virtual Reality Software and Technology

October 2023

542 pages

ISBN:9798400703287

DOI:10.1145/3611659

Copyright © 2023 ACM.

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Published: 09 October 2023

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Cited By

Liebers JLaskowski PRademaker FSabel LHoppen JGruenefeld USchneegass S(2024)Kinetic Signatures: A Systematic Investigation of Movement-Based User Identification in Virtual RealityProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642471(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642471
Rack CNair VSchach LFoschum FRoth MLatoschik M(2024)Navigating the Kinematic Maze: Analyzing, Standardizing and Unifying XR Motion Datasets2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)10.1109/VRW62533.2024.00098(507-514)Online publication date: 16-Mar-2024
https://doi.org/10.1109/VRW62533.2024.00098
Li MZafar NBanerjee NBanerjee S(2024)Evaluating Deep Networks for Detecting User Familiarity with VR from Hand Interactions2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)10.1109/AIxVR59861.2024.00036(226-230)Online publication date: 17-Jan-2024
https://doi.org/10.1109/AIxVR59861.2024.00036

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