Christian Rack

Christian Rack, Konstantin Kobs, Tamara Fernando et al.

Various machine learning approaches have proven to be useful for user verification and identification based on motion data in eXtended Reality (XR). However, their real-world application still faces significant challenges concerning versatility, i.e., in terms of extensibility and generalization capability. This article presents a solution that is both extensible to new users without expensive retraining, and that generalizes well across different sessions, devices, and user tasks. To this end, we developed a similarity-learning model and pretrained it on the "Who Is Alyx?" dataset. This dataset features a wide array of tasks and hence motions from users playing the VR game "Half-Life: Alyx". In contrast to previous works, we used a dedicated set of users for model validation and final evaluation. Furthermore, we extended this evaluation using an independent dataset that features completely different users, tasks, and three different XR devices. In comparison with a traditional classification-learning baseline, our model shows superior performance, especially in scenarios with limited enrollment data. The pretraining process allows immediate deployment in a diverse range of XR applications while maintaining high versatility. Looking ahead, our approach paves the way for easy integration of pretrained motion-based identification models in production XR systems.

Lukas Schach, Christian Rack, Ryan P. McMahan et al.

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.