Experimental comparison of one-class classifiers for online signature verification

Online signature verification systems based on one-class classifiers are presented. Global information is extracted with a feature-based representation and recognized by using an ensemble of one-class classifiers. Experimental results obtained on the SUBCORPUS-100 MCYT signature database (100 signers, 5000 signatures) show that the machine experts, here proposed, outperform the state-of-the-art works both for random and skilled forgeries.