Automatic signature verification and writer identification — the state of the art

In recent years, along with the extraordinary diffusion of the Internet and a growing need for personal verification in many daily applications, automatic signature verification is being considered with renewed interest. This paper presents the state of the art in automatic signature verification. It addresses the most valuable results obtained so far and highlights the most profitable directions of research to date. It includes a comprehensive bibliography of more than 300 selected references as an aid for researchers working in the field.

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Automatic Signature Verification: The State of the Art

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

This paper describes a novel approach for signature verification and identification in an offline environment based on a quasi-multiresolution technique using GSC (Gradient, Structural and Concavity) features for feature extraction. These features when used at the word level, instead of the character level, yield promising results with accuracies as high as 78% and 93% for verification and identification, respectively. This method was successfully employed in our previous theory of individuality of handwriting developed at CEDAR — based on obtaining within and between writer statistical distance distributions. In this paper, exploring signature verification and identification as offline handwriting verification and identification tasks respectively, we depict a mapping from the handwriting domain to the signature domain.

Offline signature verification and identification using distance statistics

We present a detailed and up-to-date survey of the literature on parallel branch-and-bound algorithms. We synthesize previous work in this area and propose a new classification of parallel branch-and-bound algorithms. This classification is used to analyze the methods proposed in the literature. To facilitate our analysis, we give a new characterization of branch-and-bound algorithms, which consists of isolating the performed operations without specifying any particular order for their execution.

Parallel Branch-and-Branch Algorithms: Survey and Synthesis

Structural description and classification of signature images

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

In this paper, we propose a parallelized computational scheme called Parallelized Depth-First Algorithm (PDFA) for Branch-and-Bound (B&B) method that works on multiprocessor systems. It is shown theoretically that the space requirement of PDFA on p processing units is at most p times as much as that of the sequential B&B algorithm with the depth-first search function. Moreover, from our experimental results through simulation, it is known that the computation time of PDFA on p processing units can be resuced to less than 1/p that of the sequential B&B algorithm with the depth-first search function. We name this reduction effect in the computation time Acceleration Effect.

A parallel searching scheme for multiprocessor systems and its application to combinatorial problems

an urban map, the procedure composes the road-network by searching the roads from each intersection. Next, it In this paper, we propose an experimental method to modifies the road-network by interpreting the original urrecognize roads from an urban map on the basis of the ban map and the network simultaneously with heuristic road-network. The road-network is a conceptual graph knowledges about the urban map. to represent connective relationships among roads. The first step in our recognition procedure is to construct this road-network approximately by the bottom-up method, as an application of image processing techniques. Then, the next step is t o modify and complement this roadnetwork exactly, based on both the heuristic knowledges for urban maps and the consistent/inconsistent interpretations for the road-network itself, from a top-down point of view. Such an approach integrated cooperatively with the top-down and bottom-up methods makes it successful to recognize roa.ds in comparison with many traditional bottom-up oriented approaches. Also, we confirm our approach through some experiments.

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Yuuji Yoshida

Papers

Structural description and classification of signature images

Off-line preprocessing and verification of signatures

Feature extraction and selection for simulated signature verification

A parallel searching scheme for multiprocessor systems and its application to combinatorial problems

Recognition of Roads in an Urban Map by Using the Topological Road-Network.