Deep Learning for Virtual Reality User Identification: A Benchmark
Virtual Reality (VR) applications require robust user identification systems to ensure secure access to equipment and protect worker identities. Motion tracking data from VR headsets and controllers has emerged as a powerful behavioral biometric, with recent studies demonstrating identification accuracies exceeding 94% across a large user base. However, the application of modern deep learning architectures, particularly State Space Models (SSM), to VR scenarios remains largely unexplored. In this work, we benchmark user identification performance across the large-scale Who is Alyx VR dataset, gathering data from 71 users playing the popular Half-Life:Alyx game. We evaluate both establishe...
| Authors | Davide Frizzo, Fabrizio Genilotti, David Petrovic et al. |
|---|---|
| Primary category | cs.HC |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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