An advanced multi-matcher method for on-line signature verification featuring global features and tokenised random numbers

In this work, we propose a multi-matcher method for on-line signature verification that combines bi-class classifiers and one-class classifiers. Global information is extracted with a feature-based representation and recognized by using an ensemble of classifiers. Moreover, we show that methods based on tokenised pseudo-random numbers and user specific signature features are highly dependent upon a parameter, the hashing threshold; we demonstrate that using an ensemble of classifiers it is possible to solve this problem leading to a considerable performance improvement.