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 and pertinent features. The main difficulty lies in the local variability of the writing trace of the signature which is closely related to the identity of human beings. In this paper, we propose a new formalism for signature representation based on visual perception. A signature image consists of 512×128 pixels and is centered on a grid of rectangular retinas which are excited by local portions of the signature. Granulometric size distributions are used for the definition of local shape descriptors in an attempt to characterize the amount of signal activity exciting each retina on the focus of the attention grid. Experimental 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 random forgeries