用动作识别用户，跨应用识别效果差
Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning
- 基于动作数据训练分类与相似性模型
- 同应用内识别准确，跨应用性能显著下降
- 适合研究元宇宙中动作生物识别风险
本文研究了两种先进分类与相似性学习模型在多种扩展现实（XR）应用中基于用户动作进行身份识别的泛化能力。我们构建了一个新数据集，包含49名用户在五种不同XR应用中的动作数据：四种具有不同任务和动作模式的XR游戏，以及一种无预设任务的社会类XR应用。该数据集用于评估模型在不同应用间的性能表现，特别是泛化能力。结果显示，尽管模型在单一应用内能准确识别个体，但在跨应用场景下的识别能力仍然有限。研究揭示了当前模型的泛化局限性，为动作识别作为元宇宙中用户验证与身份识别手段提供了重要参考，并警示了潜在的滥用风险。相关跨应用XR动作数据集与代码已公开，以推动典型元宇宙使用场景下动作识别泛化研究。
原文摘要 · Abstract (English)
This paper examines the generalization capacity of two state-of-the-art classification and similarity learning models in reliably identifying users based on their motions in various Extended Reality (XR) applications. We developed a novel dataset containing a wide range of motion data from 49 users in five different XR applications: four XR games with distinct tasks and action patterns, and an additional social XR application with no predefined task sets. The dataset is used to evaluate the performance and, in particular, the generalization capacity of the two models across applications. Our results indicate that while the models can accurately identify individuals within the same application, their ability to identify users across different XR applications remains limited. Overall, our results provide insight into current models generalization capabilities and suitability as biometric methods for user verification and identification. The results also serve as a much-needed risk assessment of hazardous and unwanted user identification in XR and Metaverse applications. Our cross-application XR motion dataset and code are made available to the public to encourage similar research on the generalization of motion-based user identification in typical Metaverse application use cases.
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