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Emerging Virtual Reality (VR) displays with embedded eye trackers are currently becoming a commodity hardware (e.g., HTC Vive Pro Eye). Eye-tracking data can be utilized for several purposes, including gaze monitoring, privacy protection, and user authentication/identification. Identifying users is an integral part of many applications due to security and privacy concerns. In this paper, we explore methods and eye-tracking features that can be used to identify users. Prior VR researchers explored machine learning on motion-based data (such as body motion, head tracking, eye tracking, and hand tracking data) to identify users. Such systems usually require an explicit VR task and many features to train the machine learning model for user identification. We propose a system to identify users utilizing minimal eye-gaze-based features without designing any identification-specific tasks. We collected gaze data from an educational VR application and tested our system with two machine learning (ML) models, random forest (RF) and k-nearest-neighbors (kNN), and two deep learning (DL) models: convolutional neural networks (CNN) and long short-term memory (LSTM). Our results show that ML and DL models could identify users with over 98% accuracy with only six simple eye-gaze features. We discuss our results, their implications on security and privacy, and the limitations of our work.
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Who do you look like? - Gaze-based authentication for workers in VR
Behavior-based authentication methods are actively being developed for XR. In particular, gaze-based methods promise continuous authentication of remote users. However, gaze behavior depends on the task being performed. Identification rate is typically highest when comparing data from the same task. In this study, we compared authentication performance using VR gaze data during random dot viewing, 360-degree image viewing, and a nuclear training simulation. We found that within-task authentication performed best for image viewing (72%). The implication for practitioners is to integrate image viewing into a VR workflow to collect gaze data that is viable for authentication.
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- Award ID(s):
- 2026540
- PAR ID:
- 10354972
- Date Published:
- Journal Name:
- 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)
- Page Range / eLocation ID:
- 744 to 745
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VRW55335.2022.00223
