KVC-onGoing: Keystroke Verification Challenge

• URL: http://arxiv.org/abs/2412.20530v1
• Date: Sun, 29 Dec 2024 17:35:01 GMT
• Title: KVC-onGoing: Keystroke Verification Challenge
• Authors: Giuseppe Stragapede, Ruben Vera-Rodriguez, Ruben Tolosana, Aythami Morales, Ivan DeAndres-Tame, Naser Damer, Julian Fierrez, Javier Ortega-Garcia, Alejandro Acien, Nahuel Gonzalez, Andrei Shadrikov, Dmitrii Gordin, Leon Schmitt, Daniel Wimmer, Christoph Großmann, Joerdis Krieger, Florian Heinz, Ron Krestel, Christoffer Mayer, Simon Haberl, Helena Gschrey, Yosuke Yamagishi, Sanjay Saha, Sanka Rasnayaka, Sandareka Wickramanayake, Terence Sim, Weronika Gutfeter, Adam Baran, Mateusz Krzysztoń, Przemysław Jaskóła,
• Abstract summary: Keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects. Results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics.
• Score: 38.38601228338356
• License:
• Abstract: This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects' age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab.

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