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This project has received funding from the Project INTERACTION (PID2021-126521OB-I00 MICINN/FEDER), Project Human-CAIC (TED2021-131787BI00 MICINN), and from the Autonomous Community of Madrid (ELLIS Unit Madrid).

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KVC-onGoing: Keystroke Verification Challenge

Publicado en:PATTERN RECOGNITION. 161 111287- - 2025-05-01 161(), DOI: 10.1016/j.patcog.2024.111287

Autores: Stragapede, Giuseppe; Vera-Rodriguez, Ruben; Tolosana, Ruben; Morales, Aythami; DeAndres-Tame, Ivan; Damer, Naser; Fierrez, Julian; Ortega-Garcia, Javier; Acien, Alejandro; Gonzalez, Nahuel; Shadrikov, Andrei; Gordin, Dmitrii; Schmitt, Leon; Wimmer, Daniel; Grossmann, Christoph; Krieger, Joerdis; Heinz, Florian; Krestel, Ron; Mayer, Christoffer; Haberl, Simon; Gschrey, Helena; Yamagishi, Yosuke; Saha, Sanjay; Rasnayaka, Sanka; Wickramanayake, Sandareka; Sim, Terence; Gutfeter, Weronika; Baran, Adam; Krzyszton, Mateusz; Jaskola, Przemyslaw

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This article presents the Keystroke Verification Challenge- onGoing (KVC-onGoing)1, 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 CodaLab2.

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