OFF-LINE SIGNATURE VERIFICATION BY LOCAL GRANULOMETRIC SIZE DISTRIBUTIONS

A fundamental problem in the field of off-line signature verification is the lack of a signature representation based on shape descriptors a nd pertinent features. The main difficulty lies in the local variabili ty of the writing trace of the signature which is closely related to t he identity of human beings, In this paper, we propose a new formalism for signature representation based on visual perception. A signature image consists of 512 x 128 pixels and is centered on a grid of rectan gular retinas which are excited by local portions of the signature. Gr anulometric size distributions are used for the definition of local sh ape descriptors in an attempt to characterize the amount of signal act ivity exciting each retina on the focus of the attention grid. Experim ental evaluation of this scheme is made using a signature database of 800 genuine signatures from 20 individuals. Two types of classifiers, a Nearest Neighbor and a threshold classifier, show a total error rate below 0.02 percent and 1.0 percent, respectively, in the context of r andom forgeries.