IEEE BigData 2023 Keystroke Verification Challenge (KVC)

• URL: http://arxiv.org/abs/2401.16559v1
• Date: Mon, 29 Jan 2024 20:51:42 GMT
• Title: IEEE BigData 2023 Keystroke Verification Challenge (KVC)
• Authors: Giuseppe Stragapede and Ruben Vera-Rodriguez and Ruben Tolosana and Aythami Morales and Ivan DeAndres-Tame and Naser Damer and Julian Fierrez and Javier-Ortega Garcia and Nahuel Gonzalez and Andrei Shadrikov and Dmitrii Gordin and Leon Schmitt and Daniel Wimmer and Christoph Grossmann and Joerdis Krieger and Florian Heinz and Ron Krestel and Christoffer Mayer and Simon Haberl and Helena Gschrey and Yosuke Yamagishi and Sanjay Saha and Sanka Rasnayaka and Sandareka Wickramanayake and Terence Sim and Weronika Gutfeter and Adam Baran and Mateusz Krzyszton and Przemyslaw Jaskola
• Abstract summary: This paper considers the biometric verification performance of Keystroke Dynamics 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. 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.
• Score: 14.366081634293721
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge (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 CodaLab, 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.

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