Putz-Leszczyńska, J; Pacut, A
Handwritten signature verification algorithms are designed to distinguish between genuine signatures and forgeries. One of the central issues with such algorithms is the unavailability of skilled forgeries during the template creation. As a solution, we propose the idea of universal forgery features, where a global classifier is used to classify a signature as a genuine one or, as a forgery, without the actual knowledge of the signature template and its owner. This classifier is trained once, during the system tuning on a group of historical data. A global classifier trained on a set of training signatures is not be additionally trained after implementation; in other words, additional users enrollments have no effect on the global classifier parameters. This idea effectively solves the issue of the lack of skilled forgeries during template creation.