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1 Equipe Monétique & Biométrie - Laboratoire GREYC - UMR6072 GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen

Abstract : Among all the existing biometric modalities, authentication systems based on keystroke dynamics present interesting advantages. These solutions are well accepted by users and cheap as no additional sensor is required for authenticating the user before accessing to an application. In the last thirty years, many researchers have proposed, di erent algorithms aimed at increasing the performance of this approach. Their main drawback lies on the large number of data required for the enrollment step. As a consequence, the veri cation system is barely usable, because the enrollment is too restrictive. In this work, we propose a new method based on the Support Vector Machine SVM learning satisfying industrial conditions i.e., few samples per user are needed during the enrollment phase to create its template. In this method, users are authenticated through the keystroke dynamics of a shared secret chosen by the system administrator. We use the GREYC keystroke database that is composed of a large number of users 100 for validation purposes. We compared the proposed method with six methods from the literature selected based on their ability to work with few enrollment samples. Experimental results show that, eventhough the computation time to build the template can be longer with our method 54 seconds against 3 seconds for most of the others, its performance outperforms the other methods in an industrial context Equal Error Rate of 15.28% against 16.79% and 17.02% for the two best methods of the state-of-the-art, on our dataset and ve samples to create the template, with a better computation time than the second best method.

Keywords : Biometrics Authentication Keystroke dynamics Support Vector Machine learning





Author: Romain Giot - Mohamad El-Abed - Baptiste Hemery - Christophe Rosenberger -

Source: https://hal.archives-ouvertes.fr/



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