Versatile User Identification in Extended Reality using Pretrained Similarity-Learning

arXiv:2302.07517v5 Announce Type: replace-cross
Abstract: Different approaches in machine learning have proven 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 …

abstract application article arxiv capability challenges cs.hc cs.lg data extended reality however identification machine machine learning reality similarity-learning solution terms type verification world