Who is Alyx? A new behavioral biometric dataset for user identification in XR
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 sep...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Virtual Reality |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frvir.2023.1272234/full |
| _version_ | 1797629452132286464 |
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| author | Christian Rack Tamara Fernando Murat Yalcin Andreas Hotho Marc Erich Latoschik |
| author_facet | Christian Rack Tamara Fernando Murat Yalcin Andreas Hotho Marc Erich Latoschik |
| author_sort | Christian Rack |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:53:31Z |
| format | Article |
| id | doaj.art-cbb8fab8e3ec4e5b8d5ace5c7c4b47e5 |
| institution | Directory Open Access Journal |
| issn | 2673-4192 |
| language | English |
| last_indexed | 2024-03-11T10:53:31Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Virtual Reality |
| spelling | doaj.art-cbb8fab8e3ec4e5b8d5ace5c7c4b47e52023-11-13T12:38:05ZengFrontiers Media S.A.Frontiers in Virtual Reality2673-41922023-11-01410.3389/frvir.2023.12722341272234Who is Alyx? A new behavioral biometric dataset for user identification in XRChristian Rack0Tamara Fernando1Murat Yalcin2Andreas Hotho3Marc Erich Latoschik4Human-Computer Interaction (HCI) Group, Informatik, University of Würzburg, Würzburg, GermanyHuman-Computer Interaction (HCI) Group, Informatik, University of Würzburg, Würzburg, GermanyHuman-Computer Interaction (HCI) Group, Informatik, University of Würzburg, Würzburg, GermanyData Science Chair, Informatik, University of Würzburg, Würzburg, GermanyHuman-Computer Interaction (HCI) Group, Informatik, University of Würzburg, Würzburg, GermanyIntroduction: 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.https://www.frontiersin.org/articles/10.3389/frvir.2023.1272234/fulldatasetbehaviometricdeep learninguser identificationphysiological dataset |
| spellingShingle | Christian Rack Tamara Fernando Murat Yalcin Andreas Hotho Marc Erich Latoschik Who is Alyx? A new behavioral biometric dataset for user identification in XR Frontiers in Virtual Reality dataset behaviometric deep learning user identification physiological dataset |
| title | Who is Alyx? A new behavioral biometric dataset for user identification in XR |
| title_full | Who is Alyx? A new behavioral biometric dataset for user identification in XR |
| title_fullStr | Who is Alyx? A new behavioral biometric dataset for user identification in XR |
| title_full_unstemmed | Who is Alyx? A new behavioral biometric dataset for user identification in XR |
| title_short | Who is Alyx? A new behavioral biometric dataset for user identification in XR |
| title_sort | who is alyx a new behavioral biometric dataset for user identification in xr |
| topic | dataset behaviometric deep learning user identification physiological dataset |
| url | https://www.frontiersin.org/articles/10.3389/frvir.2023.1272234/full |
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