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  4. IEEE BigData 2023 Keystroke Verification Challenge (KVC)
 
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2023
Conference Paper
Title

IEEE BigData 2023 Keystroke Verification Challenge (KVC)

Abstract
This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge1 (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the
largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab2, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.
Author(s)
Stragapede, Giuseppe
Universidad Autonoma de Madrid
Vera-Rodriguez, Ruben
Universidad Autonoma de Madrid
Tolosana, Ruben
Universidad Autonoma de Madrid
Morales , Aythami
Universidad Autonoma de Madrid
DeAndres-Tame, Ivan
Universidad Autonoma de Madrid
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Fierrez, Julian
Universidad Autonoma de Madrid
Garcia, Javier-Ortega
Universidad Autonoma de Madrid
Gonzalez, Nahuel
Universidad de Buenos Aires
Shadrikov, Andrei
Verigram LLC
Gordin, Dmitrii
Citix, Almaty
Schmitt, Leon
Regensburg University of Applied Sciences
Wimmer, Daniel
Ostbayerische Technische Hochschule Regensburg
Großmann, Christoph
Regensburg University of Applied Sciences
Krieger, Joerdis
Regensburg University of Applied Sciences
Heinz, Florian
Regensburg University of Applied Sciences
Krestel, Ron
Regensburg University of Applied Sciences
Mayer, Christoffer
Regensburg University of Applied Sciences
Haberl, Simon
Regensburg University of Applied Sciences
Gschrey, Helena
Regensburg University of Applied Sciences
Yamagishi, Yosuke
Regensburg University of Applied Sciences
Saha, Sanjay
Rasnayaka, Sanka
Wickramanayake, Sandareka
University of Moratuwa
Sim, Terence
Gutfeter, Weronika
NASK - National Research
Baran, Adam
NASK - National Research
Krzysztón, Mateusz
NASK - National Research
Jaskóła, Przemysław
NASK - National Research
Mainwork
IEEE International Conference on Big Data 2023. Proceedings  
Project(s)
Next Generation Biometric Systems  
Next Generation Biometric Systems  
INTER-ACTION (PID2021-126521OBI00 MICINN/FEDER)
HumanCAIC (TED2021-131787BI00 MICINN)
Funder
Bundesministerium für Bildung und Forschung -BMBF-
Hessisches Ministerium für Wissenschaft und Kunst -HMWK-  
European Commission  
European Commission  
Conference
International Conference on Big Data 2023  
Open Access
DOI
10.1109/BigData59044.2023.10386557
Additional link
Full text
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Information Technology

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • Research Line: Machine learning (ML)

  • LTA: Interactive decision-making support and assistance systems

  • LTA: Machine intelligence, algorithms, and data structures (incl. semantics)

  • Biometrics

  • Authentication

  • Machine learning

  • Deep learning

  • ATHENE

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