@inproceedings{StragapedeVeraRodriguezTolosanaetal., author = {Stragapede, Giuseppe and Vera-Rodriguez, Ruben and Tolosana, Ruben and Morales, Aythami and DeAndres-Tame, Ivan and Damer, Naser and Fierrez, Julian and Garcia, Javier-Ortega and Gonzalez, Nahuel and Shadrikov, Andrei and Gordin, Dmitrii and Schmitt, Leon and Wimmer, Daniel and Großmann, Christoph and Krieger, Joerdis and Heinz, Florian and Krestel, Ron and Mayer, Christoffer and Haberl, Simon and Gschrey, Helena and Yamagishi, Yosuke and Saha, Sanjay and Rasnayaka, Sanka and Wickramanayake, Sandareka and Sim, Terence and Gutfeter, Weronika and Baran, Adam and Krzyszt{\´o}n, Mateusz and Jask{\´o}ła, Przemysław}, title = {IEEE BigData 2023 Keystroke Verification Challenge (KVC)}, series = {2023 IEEE International Conference on Big Data,15-18 December 2023, Sorrento, Italy}, booktitle = {2023 IEEE International Conference on Big Data,15-18 December 2023, Sorrento, Italy}, editor = {He, Jingrui}, publisher = {IEEE}, address = {Piscataway, NJ, USA}, isbn = {979-8-3503-2445-7}, doi = {10.1109/BigData59044.2023.10386557}, pages = {6092 -- 6100}, language = {en} } @unpublished{StragapedeVeraRodriguezTolosanaetal., author = {Stragapede, Giuseppe and Vera-Rodriguez, Ruben and Tolosana, Ruben and Morales, Aythami and DeAndres-Tame, Ivan and Damer, Naser and Fierrez, Julian and Ortega-Garcia, Javier and Acien, Alejandro and Gonz{\´a}lez, Nahuel and Shadrikov, Andrei and Gordin, Dmitrii and Schmitt, Leon and Wimmer, Daniel and Großmann, Christoph and Krieger, Joerdis and Heinz, Florian and Krestel, Ron and Mayer, Christoffer and Haberl, Simon and Gschrey, Helena and Yamagishi, Yosuke and Saha, Sanjay and Rasnayaka, Sanka and Wickramanayake, Sandareka and Sim, Terence and Gutfeter, Weronika and Baran, Adam and Krzyszton, Mateusz and Jask{\´o}ła, Przemysław}, title = {Kvc-Ongoing: Keystroke Verification Challenge}, doi = {10.2139/ssrn.4743934}, pages = {44}, 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.}, language = {en} }