The graphology applied to signature verification
01 Jan 2005-
TL;DR: Some of the main features of the graphology are described and a set of features to automatic signature verification are proposed and evaluated in a database of 5,600 signatures using hidden Markov models.
Abstract: In this paper we discuss automatic signature verification in the context of the graphology. Graphology is claimed to be useful for everything from understanding health issues, morality and past experiences to hidden talents, and mental problems. It is not restricted to this, though. Forensic document examiners use the concepts of graphology to examine handwriting in order to detect authenticity or forgery. In this work, we describe some of the main features of the graphology and propose a set of features to automatic signature verification. They are evaluated in a database of 5,600 signatures using hidden Markov models.
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01 Sep 2008
TL;DR: This paper presents the state of the art in automatic signature verification and addresses the most valuable results obtained so far and highlights the most profitable directions of research to date.
Abstract: 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.
688 citations
Cites background from "The graphology applied to signature..."
...eter features and some of the main features used by forensic experts [70], [99], [101], [236], [295], [303]–[305]....
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...racy required of banks and other financial institutions is an area of continued research [39], [59], [60], [61], [171], [236], [367]....
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TL;DR: A novel formulation of the problem that includes knowledge of skilled forgeries from a subset of users in the feature learning process, that aims to capture visual cues that distinguish genuine signatures and forgeries regardless of the user is proposed.
252 citations
TL;DR: A method for conducting off-line handwritten signature verification works at the global image level and measures the grey level variations in the image using statistical texture features using the co-occurrence matrix and local binary pattern.
212 citations
01 Nov 2017
TL;DR: How the problem has been handled in the past few decades is presented, the recent advancements in the field are analyzed, and the potential directions for future research are analyzed.
Abstract: The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.
135 citations
TL;DR: In this article, the authors present how the signature verification problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.
Abstract: The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.
132 citations
References
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TL;DR: This paper summarizes the activity from year 1989 to 1993 in automatic signature verification and reports on the different projects dealing with dynamic, static and neural network approaches.
Abstract: 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.
468 citations
"The graphology applied to signature..." refers methods in this paper
...Some of the concepts of graphology have been intrinsically used to build automatic signature verification systems by several different authors [5, 4, 3, 6]....
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TL;DR: The role of signature shape description and shape similarity measure is discussed in the context of signature recognition and verification and the proposed method allows definite training control and at the same time significantly reduces the number of enrollment samples required to achieve a good performance.
192 citations
"The graphology applied to signature..." refers methods in this paper
...Some of the concepts of graphology have been intrinsically used to build automatic signature verification systems by several different authors [5, 4, 3, 6]....
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TL;DR: This paper proposes a new formalism for signature representation based on visual perception, and two types of classifiers, a nearest neighbor and a threshold classifier, show a total error rate below 0.02 percent and 1.0 percent in the context of random forgeries.
Abstract: 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.
154 citations
"The graphology applied to signature..." refers methods in this paper
...A more complex approach, but based on the same idea was proposed by Sabourin et al [7]....
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Book•
01 Jan 1928
11 citations
"The graphology applied to signature..." refers background in this paper
...He put of the order in Michon ́s work and divided the writing into seven fundamental elements: speed, pressure, form, dimension, continuity, direction, and order [1]....
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