Do synthetic generated signatures reflect the subject motor programs? A pilot study

Abstract

Handwritten signature is a biometric trait used for verifying a person’s identity. Automatic signature verification systems typically require a lot of specimens in order to model the signing habit of a subject but, in a real scenario, few signature samples are available. To overcome this problem, methods for creating human-like duplicated signatures using one real signature per subject and based on sigma lognormal decomposition have been proposed in literature. In this paper, we evaluate if duplicated signatures show the same amount of variability observed in real signaturesby detectingand analysingsignature stability regions. In particular, we investigateif real and duplicated signatures could be the instances of a similarmotor program. Experimental results on a standard dataset show thatin some casesduplication methods introduce a variability that is greater thanthe writer's variability to such an extent to generate motor programs that do not belong to the writer's repertoire. Results suggest that a connectionexists between trajectory plan and motor plan parameters, whichcannot be modified independently one from the other in order to generate synthetic signatures that reflect the writer’smotor program repertoire.