Offline continuous adaptation of templates for signature identification

Automatic on-line signature identification is a procedure which allows a machine to identify a subject among a cohort of individuals by using only the subject's signature. The following paper deals with features and models required in order to allow a machine to learn and discriminate people on the basis of such a biometric trait. The proposed solution presents a neural network based framework for template adaptation which has demonstrated to improve the resilience of a system, when it has to face with forgeries, that is, fake signatures which are used in order to attack the system and grant unauthorized access to services. The proposed framework provides an improved security level of 35% with respect to non adapted systems.