Abstract
In this paper, we propose a method to realize a classification of keystroke dynamics users before performing user authentication. The objective is to set automatically the individual parameters of the classification method for each class of users. Features are extracted from each user learning set, and then a clustering algorithm divides the user set in clusters. A set of parameters is estimated for each cluster. Authentication is then realized in a two steps process. First the users are associated to a cluster and second, the parameters of this cluster are used during the authentication step. This two steps process provides better results than system using global settings.
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Hocquet, S., Ramel, JY., Cardot, H. (2007). User Classification for Keystroke Dynamics Authentication. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_56
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DOI: https://doi.org/10.1007/978-3-540-74549-5_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74548-8
Online ISBN: 978-3-540-74549-5
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