Social gaze fingerprints: identifying social virtual reality users by their eye gaze patterns

2025/09/02 by Marius Rubo, Gayoung Son · 1 voice
Neuroscience · Psychology · #Face Recognition and Perception #Deception detection and forensic psychology #Social Robot Interaction and HRI

paper · pdf · doi:10.1007/s10055-025-01210-4

openalex publication_date 2025/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23

Abstract

Abstract Previous research demonstrated the possibility for implicit user identification in virtual reality (VR) based on motion data, but little is known about user identifiability during interactions in Social VR where users have no behavioral task and may show comparatively little head and body movements. Here we used Siamese Convolutional Neural Networks (CNNs) to test user identification based on eye gaze patterns during conversations in 128 participants. Combining separately trained models for gaze while speaking and gaze while listening to a partner allowed for a 95.50% identification accuracy. Gaze patterns were only weakly associated with self-reported personal characteristics and likely represent idiosyncratic behavior. The possibility to identify users in Social VR merely along eye gaze behavior in dyadic interactions substantiates concerns regarding data privacy and security in such environments. The technique can be applied to gaze data as they are commonly received from other users within Social VR environments where sample rate may be lower and less stable compared to data obtained in research laboratories. Future research is needed to assess how identification procedures generalize across a wider range of settings. Graphical abstract

Citations

• Development and validation of measures of social phobia scrutiny fear and social interaction anxiety11Editor’s note: This article was written before the development of some contemporary measures of social phobia, such as the Social Phobia and Anxiety Inventory (Turner et al., 1989). We have invited this article for publication because of the growing interest in the scales described therein. S.T.
• Moving and looming stimuli capture attention
• A social comparison scale: Psychometric properties and relationship to psychopathology
• Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women.
• Super-recognizers: People with extraordinary face recognition ability
• Social virtual reality elicits natural interaction behavior with self-similar and generic avatars
• The European Union general data protection regulation: what it is and what it means
• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
• ImageNet classification with deep convolutional neural networks
• Subjective social status: reliability and predictive utility for global health
• The Experimental Generation of Interpersonal Closeness: A Procedure and Some Preliminary Findings

Discussions

• Can eye‑gaze alone reveal identity in Social VR? New research found convolutional neutral networks could identify 95.5% users from their gaze data during a virtual conversation. doi.org/10.1007/s100. [bsky, 1 points, 0 comments]

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