Dynamic Signature Verification Using Pattern Recognition

ABSTRACT In this work we describe a new approach to dynamic signature verification using the discriminative training framework. The authentic and forgery samples are represented by two separate Gaussian Mixture models and discriminative training is used to achieve optimal separation between the two models. An enrollment sample clustering and screening procedure is described which improves the robustness of the system. We also introduce a method to estimate and apply subject norms representing the "typical": variation of the subject's signatures. The subject norm functions are parameterized, an…