Versatile User Identification in Extended Reality using Pretrained Similarity-Learning

TL;DR

This paper introduces a pretrained similarity-learning model for user identification in XR that is highly versatile, generalizes across sessions and devices, and requires minimal retraining for new users.

Contribution

We developed a pretrained similarity-learning approach that outperforms traditional classifiers, enabling easy integration and robust user identification in diverse XR scenarios.

Findings

Superior performance over baseline classifiers

Effective with limited enrollment data

Generalizes well across different devices and tasks

Abstract

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.