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In an off-line signature verification method based on personal models, an important issue is the number of genuine samples required to train the writer's model. In a real application, we are usually quite limited in the number of samples we can use for training [Cha, S., 2001, Baltzakis, H. et al., 2001, Yingyong, Q. et al., 1994]. Classifiers like the neural network [Baltzakis, H. et al., 2001], the hidden Markov model [Justino, E.J.R. et al., 2001] and the support vector machine [Justino, E.J.R. et al., 2003] need a substantial number of samples to produce a robust model in the training phase. This paper reports on a global method based on only two classes of models, the genuine signature and the forgery. The main objective of this method is to reduce the number of signature samples required by each writer in the training phase. For this purpose, a set of graphometric features and a neural network (NN) classifier are used.