Advanced methods for two-class problem formulation for on-line signature verification

We present several systems for on-line signature verification that approach the problem as a two-class pattern recognition problem. To our knowledge, this is the first work that solves the problem of on-line signature verification as a two-class problem using global (and not local) features. The feature vector obtained by global features is then classified into one of the two classes (genuine or impostor) by a support vector machine. Moreover, we show the combination of the systems introduced in this work permit a dramatic reduction of the equal error rate.