CC BY 4.0Stragapede, GiuseppeGiuseppeStragapedeVera-Rodriguez, RubenRubenVera-RodriguezTolosana, RubenRubenTolosanaMorales, AythamiAythamiMoralesDeAndres-Tame, IvanIvanDeAndres-TameDamer, NaserNaserDamerFierrez, JulianJulianFierrezOrtega-Garcia, JavierJavierOrtega-GarciaAcien, AlejandroAlejandroAcienGonzalez, NahuelNahuelGonzalezShadrikov, AndreiAndreiShadrikovGordin, DmitriiDmitriiGordinSchmitt, LeonLeonSchmittWimmer, DanielDanielWimmerGroßmann, ChristophChristophGroßmannKrieger, JoerdisJoerdisKriegerHeinz, FlorianFlorianHeinzKrestel, RonRonKrestelMayer, ChristofferChristofferMayerHaberl, SimonSimonHaberlGschrey, HelenaHelenaGschreyYamagishi, YosukeYosukeYamagishiWickramanayake, SandarekaSandarekaWickramanayakeSaha, SanjaySanjaySahaRasnayaka, SankaSankaRasnayakaGutfeter, WeronikaWeronikaGutfeterSim, TerenceTerenceSimBaran, AdamAdamBaranKrzysztoń, MateuszMateuszKrzysztońJaskóła, PrzemysławPrzemysławJaskóła2025-01-072025-01-072025https://doi.org/10.24406/publica-4031https://publica.fraunhofer.de/handle/publica/48107210.1016/j.patcog.2024.11128710.24406/publica-4031This 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 .enBranche: Information TechnologyResearch Line: Computer vision (CV)Research Line: Human computer interaction (HCI)Research Line: Machine learning (ML)LTA: Interactive decision-making support and assistance systemsLTA: Machine intelligence, algorithms, and data structures (incl. semantics)BiometricsMachine learningDeep learningATHENEjournal article