Abstract

In this paper we propose a new algorithm for on-line signature verification using characteristic global features values. It is based on so-called global features which describe characteristic attributes of the signature, e.g. time of signing process, number of pen-ups, average velocity of the pen etc. Our method assumes evaluation of the global features for the individual and selection of the most characteristic ones, which are used during classification phase (verification of the signature). Classification is performed using specially designed flexible neuro-fuzzy one class classifier.

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