This paper is a follow up to an article published in 1989 by R. Plamondon and G. Lorette on the state of the art in automatic signature verification and writer identification. It summarizes the activity from year 1989 to 1993 in automatic signature verification. For this purpose, we report on the different projects dealing with dynamic, static and neural network approaches. In each section, a brief description of the major investigations is given.

Automatic signature verification: the state of the art—1989–1993

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/spl times/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.

https://www.etsmtl.ca/ETS/media/ImagesETS/Labo/LIVIA/Publications/1997/sabourin_97_pami.pdf

Off-line signature verification by local granulometric size distributions

Signature verification using global and grid features

Off-line arabic signature recognition and verification

Antiforgery: a novel pseudo-outer product based fuzzy neural network driven signature verification system

This paper introduces an effective method for signature separation from nonhomogeneous noisy background. It also introduces a solution to the problem of simulated signature verification in off-line systems. Extraction of shape and density features and the effectiveness of using each and both of them are discussed in the light of experimental results.

Off-line preprocessing and verification of signatures

Feature extraction and selection for simulated signature verification

We introduce in this paper what can be considered a new trend in expert systems field. It is generating different expert systems using the same software platform developed for this purpose, and called “Expert Systems Generating Machine for Image Processing Applications ESGMIPA”. The machine is used to generate different expert systems in completely different application fields which indicates the feasibility of the proposal. Using what we called Domain Expert Guided Heuristic Search (DEGHS) and the machine, we generated an expert system that succeeded in cases where no algorithmic approach can be applied. Generating different expert systems using the same machine depends on the well-known fact that the function of an expert system is determined mainly by its knowledge base. The machine developed expedite very much the development of the expert system to reach best performance. The role of domain expert and the positive effect of the interaction between different domain experts in different fields is highlighted.

/pdf/expert-systems-generating-machine-for-image-processing-2heu5j6a.pdf

Expert Systems Generating Machine for Image Processing Applications

A BSTRACT Detecting specific types of bacteria in a microscopic Bacteria Colony Image (BCI) is a tedious and time-consuming task. Based on the previous experience in the field of computerized signature and handwriting analysis, we generated two expert systems to detect specific types of bacteria in BCI and to extract handwriting from binary bank checks images using the same software platform developed for this purpose. Using the Domain Expert Guided Heuristic Search (DEGHS) and the platform, we generated an expert system that succeeded in cases where no algorithmic approach can be applied. We exploited the fact that the function of an expert system is determined mainly by its knowledge base to generate different expert systems by modifying the content of the knowledge base. The mechanism used expedite very much the development of the expert system to reach its best performance.

G enerating e xpert s ystems to d etect s pecific b acteria t ypes and e xtract h andwriting from b anck c hecks

T his paper introduces a new method for describing signature images. The description in- volves features of the s ignatures and relations among its parts organized in a hierarchical s truc- ture (top-down analysis). The description is used to classify signature images.

Application to their classification

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