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Who is Alyx? A new behavioral biometric dataset for user identification in XR
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-353979
- 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 NetworksIntroduction: 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.…
Autor(en): | Christian Rack, Tamara Fernando, Murat Yalcin, Andreas Hotho, Marc Erich Latoschik |
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URN: | urn:nbn:de:bvb:20-opus-353979 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Frontiers in Virtual Reality |
ISSN: | 2673-4192 |
Erscheinungsjahr: | 2023 |
Band / Jahrgang: | 4 |
Aufsatznummer: | 1272234 |
Originalveröffentlichung / Quelle: | Frontiers in Virtual Reality (2023) 4:1272234. https://doi.org/10.3389/frvir.2023.1272234 |
DOI: | https://doi.org/10.3389/frvir.2023.1272234 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 005 Computerprogrammierung, Programme, Daten |
Freie Schlagwort(e): | behaviometric; dataset; deep learning; physiological dataset; user identification |
Datum der Freischaltung: | 08.05.2024 |
Datum der Erstveröffentlichung: | 10.11.2023 |
Lizenz (Deutsch): | ![]() |