用深度学习分析VR动作数据，实现94%以上精准用户识别。
Deep Learning for Virtual Reality User Identification: A Benchmark
- 对比多种深度模型，用时间序列动作数据识别用户
- 在71人数据集上达到超94%识别准确率
- 为制造业隐私保护认证提供首个完整性能基准
虚拟现实（VR）应用需可靠的用户识别系统以保障设备安全与身份隐私。来自VR头显和控制器的动作追踪数据已成为强大的行为生物特征，近期研究已在大规模用户群体中实现超过94%的识别准确率。然而，现代深度学习架构尤其是状态空间模型（SSM）在VR场景中的应用仍鲜有探索。本文基于大规模Who is Alyx VR数据集，收集了71名用户在《半衰期：爱莉克斯》游戏中的数据，评估了经典架构（LSTM、GRU、CNN、TCN、Transformer）与新兴的SSM在时序动作数据上的用户识别性能。结果提供了针对VR用户识别的首个全面基准，建立了未来制造环境隐私保护认证系统的基线性能指标。
原文摘要 · Abstract (English)
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.
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