TY - CHAP A1 - Stragapede, Giuseppe A1 - Vera-Rodriguez, Ruben A1 - Tolosana, Ruben A1 - Morales, Aythami A1 - DeAndres-Tame, Ivan A1 - Damer, Naser A1 - Fierrez, Julian A1 - Garcia, Javier-Ortega A1 - Gonzalez, Nahuel A1 - Shadrikov, Andrei A1 - Gordin, Dmitrii A1 - Schmitt, Leon A1 - Wimmer, Daniel A1 - Großmann, Christoph A1 - Krieger, Joerdis A1 - Heinz, Florian A1 - Krestel, Ron A1 - Mayer, Christoffer A1 - Haberl, Simon A1 - Gschrey, Helena A1 - Yamagishi, Yosuke A1 - Saha, Sanjay A1 - Rasnayaka, Sanka A1 - Wickramanayake, Sandareka A1 - Sim, Terence A1 - Gutfeter, Weronika A1 - Baran, Adam A1 - Krzysztón, Mateusz A1 - Jaskóła, Przemysław ED - He, Jingrui T1 - IEEE BigData 2023 Keystroke Verification Challenge (KVC) T2 - 2023 IEEE International Conference on Big Data,15-18 December 2023, Sorrento, Italy Y1 - 2023 SN - 979-8-3503-2445-7 U6 - https://doi.org/10.1109/BigData59044.2023.10386557 SP - 6092 EP - 6100 PB - IEEE CY - Piscataway, NJ, USA ER - TY - INPR A1 - Stragapede, Giuseppe A1 - Vera-Rodriguez, Ruben A1 - Tolosana, Ruben A1 - Morales, Aythami A1 - DeAndres-Tame, Ivan A1 - Damer, Naser A1 - Fierrez, Julian A1 - Ortega-Garcia, Javier A1 - Acien, Alejandro A1 - González, Nahuel A1 - Shadrikov, Andrei A1 - Gordin, Dmitrii A1 - Schmitt, Leon A1 - Wimmer, Daniel A1 - Großmann, Christoph A1 - Krieger, Joerdis A1 - Heinz, Florian A1 - Krestel, Ron A1 - Mayer, Christoffer A1 - Haberl, Simon A1 - Gschrey, Helena A1 - Yamagishi, Yosuke A1 - Saha, Sanjay A1 - Rasnayaka, Sanka A1 - Wickramanayake, Sandareka A1 - Sim, Terence A1 - Gutfeter, Weronika A1 - Baran, Adam A1 - Krzyszton, Mateusz A1 - Jaskóła, Przemysław T1 - Kvc-Ongoing: Keystroke Verification Challenge N2 - 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. Y1 - 2024 U6 - https://doi.org/10.2139/ssrn.4743934 ER -