Who Is Alyx? A new Behavioral Biometric Dataset for User Identification in XR

Abstract

This article presents a new dataset containing motion and physiological data of users playing the game "Half-Life: Alyx". The dataset specifically targets behavioral and biometric identification of XR users. It includes motion and eye-tracking data captured by a HTC Vive Pro of 71 users playing the game on two separate days for 45 minutes. Additionally, we collected physiological data from 31 of these users. We provide benchmark performances for the task of motion-based identification of XR users with two prominent state-of-the-art deep learning architectures (GRU and CNN). After training on the first session of each user, the best model can identify the 71 users in the second session with a mean accuracy of 95% within 2 minutes. The dataset is freely available under https://github.com/cschell/who-is-alyx

References in corpus (4)

• Hierarchical Text-Conditional Image Generation with CLIP Latents
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Cited by in corpus (4)

• Cultural Reflections in Virtual Reality: The Effects of User Ethnicity in Avatar Matching Experiences on Sense of Embodiment
• Effect of Duration and Delay on the Identifiability of VR Motion
• Movement- and Traffic-based User Identification in Commercial Virtual Reality Applications: Threats and Opportunities
• Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning