@article{RackFernandoYalcinetal.2023,
  author    = {Rack, Christian and Fernando, Tamara and Yalcin, Murat and Hotho, Andreas and Latoschik, Marc Erich},
  title     = {Who is Alyx? A new behavioral biometric dataset for user identification in XR},
  series = {Frontiers in Virtual Reality},
  volume    = {4},
  journal   = {Frontiers in Virtual Reality},
  issn      = {2673-4192},
  doi       = {10.3389/frvir.2023.1272234},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-353979},
  year      = {2023},
  abstract  = {Introduction: This paper addresses the need for reliable user identification in Extended Reality (XR), focusing on the scarcity of public datasets in this area. Methods: We present a new dataset collected from 71 users who played the game "Half-Life: Alyx" on an HTC Vive Pro for 45 min across two separate sessions. The dataset includes motion and eye-tracking data, along with physiological data from a subset of 31 users. Benchmark performance is established using two state-of-the-art deep learning architectures, Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Results: The best model achieved a mean accuracy of 95\% for user identification within 2 min when trained on the first session and tested on the second. Discussion: The dataset is freely available and serves as a resource for future research in XR user identification, thereby addressing a significant gap in the field. Its release aims to facilitate advancements in user identification methods and promote reproducibility in XR research.},
  language  = {en}
}