Towards Predicting Good Users for Biometric Recognition Based on Keystroke Dynamics
Entidad
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesEditor
Springer Berlin HeidelbergFecha de edición
2015Cita
10.1007/978-3-319-16181-5_54
Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II. Lecture Notes in Computer Science, Volumen 8926. Springer, 2015. 711-724
ISSN
0302-9743 (print); 1611-3349 (online)DOI
10.1007/978-3-319-16181-5_54Financiado por
This work has been partially supported by projects Bio-Shield (TEC2012-34881) from Spanish MINECO and BEAT (FP7-SEC-284989) from EU.Proyecto
info:eu-repo/grantAgreement/EC/FP7/284989; Gobierno de España. TEC2012-34881Versión del editor
http://dx.doi.org/10.1007/978-3-319-16181-5_54Materias
Keystroke; Typing patterns; Biometric; Authentication; Quality; Performance prediction; TelecomunicacionesNota
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-16181-5_54Proceedings in Computer Vision - ECCV 2014 Workshops held in Zurich (Switzerland) on 2015.
Derechos
© Springer-Verlag Berlin Heidelberg [2015]Resumen
This paper studies ways to detect good users for biometric recognition based on keystroke dynamics. Keystroke dynamics is an active research field for the biometric scientific community. Despite the great efforts made during the last decades, the performance of keystroke dynamics recognition systems is far from the performance achieved by traditional hard biometrics. This is very pronounced for some users, who generate many recognition errors even with the most sophisticate recognition algorithms. On the other hand, previous works have demonstrated that some other users behave particularly well even with the simplest recognition algorithms. Our purpose here is to study ways to distinguish such classes of users using only the genuine enrollment data. The experiments comprise a public database and two popular recognition algorithms. The results show the effectiveness of the Kullback-Leibler divergence as a quality measure to categorize users in comparison with other four statistical measures.
Lista de ficheros
Google Scholar:Morales Moreno, Aythami
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Fiérrez Aguilar, Julián
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Ortega García, Javier
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