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Author Giot, R. ♦ El-Abed, M. ♦ Rosenberger, C.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2009
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods ♦ Natural sciences & mathematics ♦ Life sciences; biology ♦ Biochemistry
Subject Keyword Support vector machines ♦ Biometrics ♦ Authentication ♦ Hidden Markov models ♦ Benchmark testing ♦ Feature extraction ♦ Control systems ♦ Timing ♦ Resource management ♦ Biological materials
Abstract Keystroke dynamics biometric systems have been studied for more than twenty years. They are very well perceived by users, they may be one of the cheapest biometric system (as no specific material is required) even if they are not commonly spread and used [1]. We propose in this paper a new method based on SVM learning satisfying operational conditions (no more than 5 captures for the enrollment step). In the proposed method, users are authenticated thanks to keystroke dynamics of a passphrase (that can be chosen by the system administrator). We use the GREYC keystroke benchmark that is composed of a large number of users (100) for validation purposes. We tested the proposed method face to four other methods from the state of the art. Experimental results show that the proposed method outperforms them in an operational context.
Description Author affiliation: Laboratoire GREYC, ENSICAEN - Université de Caen, Basse-Normandie - CNRS (Giot, R.; El-Abed, M.; Rosenberger, C.)
ISBN 9781424450190
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2009-09-28
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424450206
Size (in Bytes) 222.77 kB
Page Count 6
Starting Page 1
Ending Page 6


Source: IEEE Xplore Digital Library