Ensemble of On-line signature matchers based on OverComplete Feature generation

A novel method for building an ensemble of On-line signature verification systems based on one-class classifiers is presented. The ensemble is built concatenating the classifiers obtained by the Random Subspace on the “original features” and a set of classifiers each trained selecting a different set of “artificial features” for each different subset of users that belong to the validation set. The “artificial features” are extracted using an over-complete global feature combination, starting from a set of global features a set of artificial features is created by applying mathematical operators to a randomly extracted set of the original ones, then a small subset is selected for verification by running Sequential Forward Floating Selection (SFFS). Finally a set of One-class classifiers are used to classify, between genuine and impostor, each match between two signatures. As dataset the MCYT signature database is used, our results show that the proposed ensemble outperforms the ensembles based only on the original features. Using only 5 genuine signatures for each user our best system obtains an Equal Error Rate of 4.5 in the Skilled Forgeries and 1.4 in the Random Forgeries, when 20 genuine signatures are used to train the classifiers an Equal Error Rate of 2.2 in the Skilled Forgeries and 0.5 in the Random Forgeries are obtained.