• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. KVC-onGoing: Keystroke Verification Challenge
 
  • Details
  • Full
Options
2025
Journal Article
Title

KVC-onGoing: Keystroke Verification Challenge

Abstract
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 .
Author(s)
Stragapede, Giuseppe
Universidad Autónoma de Madrid
Vera-Rodriguez, Ruben
Universidad Autónoma de Madrid
Tolosana, Ruben
Universidad Autónoma de Madrid
Morales, Aythami
Universidad Autónoma de Madrid
DeAndres-Tame, Ivan
Universidad Autónoma de Madrid
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Fierrez, Julian
Universidad Autónoma de Madrid
Ortega-Garcia, Javier
Universidad Autónoma de Madrid
Acien, Alejandro
Universidad Autónoma de Madrid
Gonzalez, Nahuel
Universidad de Buenos Aires
Shadrikov, Andrei
Verigram LLC
Gordin, Dmitrii
Citix
Schmitt, Leon
Regensburg University of Applied Sciences
Wimmer, Daniel
Regensburg University of Applied Sciences
Großmann, Christoph
Regensburg University of Applied Sciences
Krieger, Joerdis
Regensburg University of Applied Sciences
Heinz, Florian
Regensburg University of Applied Sciences
Krestel, Ron
Regensburg University of Applied Sciences
Mayer, Christoffer
Regensburg University of Applied Sciences
Haberl, Simon
Regensburg University of Applied Sciences
Gschrey, Helena
Regensburg University of Applied Sciences
Yamagishi, Yosuke
The University of Tokyo
Wickramanayake, Sandareka
University of Moratuwa
Saha, Sanjay
National University of Singapore  
Rasnayaka, Sanka
National University of Singapore  
Gutfeter, Weronika
NASK National Research Institute
Sim, Terence
National University of Singapore  
Baran, Adam
NASK National Research Institute
Krzysztoń, Mateusz
NASK National Research Institute
Jaskóła, Przemysław
NASK National Research Institute
Journal
Pattern recognition  
Project(s)
Next Generation Biometric Systems  
Funder
Bundesministerium für Bildung und Forschung -BMBF-  
Open Access
File(s)
Download (2.13 MB)
Rights
CC BY 4.0: Creative Commons Attribution
DOI
10.1016/j.patcog.2024.111287
10.24406/publica-4031
Additional link
Full text
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Branche: Information Technology

  • Research Line: Computer vision (CV)

  • Research Line: Human computer interaction (HCI)

  • Research Line: Machine learning (ML)

  • LTA: Interactive decision-making support and assistance systems

  • LTA: Machine intelligence, algorithms, and data structures (incl. semantics)

  • Biometrics

  • Machine learning

  • Deep learning

  • ATHENE

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024