Deep Learning for Virtual Reality User Identification: A Benchmark

TL;DR

This paper benchmarks various deep learning models, including state space models, for user identification in VR using motion data, establishing baseline performance metrics for future secure authentication systems.

Contribution

It provides the first comprehensive comparison of deep learning architectures, including SSMs, for VR user identification on a large-scale dataset.

Findings

Deep learning models achieve over 94% accuracy in VR user identification.

State Space Models (SSMs) are evaluated alongside traditional architectures.

Benchmark results establish baseline metrics for future privacy-preserving VR authentication.

Abstract

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 established architectures (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Temporal Convolutional Network (TCN), Transformer) and the emerging SSMs on time series motion data. Our results provide the first comprehensive benchmark of state-of-the-art and novel architectures for VR user identification, establishing baseline performance metrics for future privacy preserving authentication systems in manufacturing environments.