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

TL;DR

This paper introduces a new behavioral biometric dataset for XR user identification, using motion and physiological data collected during gameplay, and demonstrates high accuracy with deep learning models.

Contribution

It provides a novel dataset for XR user identification and benchmarks deep learning approaches achieving 95% accuracy in user recognition.

Findings

Deep learning models achieve 95% accuracy in user identification

The dataset includes motion, eye-tracking, and physiological data from 71 users

Benchmark results establish a new standard for XR biometric identification

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