Journal Article•
Handwritten signature identification using basic concepts of graph theory
01 Oct 2011-WSEAS Transactions on Signal Processing archive (World Scientific and Engineering Academy and Society (WSEAS))-Vol. 7, Iss: 4, pp 117-129
TL;DR: Previous work in the field of signature and writer identification is presented to show the historical development of the idea and a new promising approach in handwritten signature identification based on some basic concepts of graph theory is defined.
Abstract: Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.
Citations
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TL;DR: The approaches taken from other researches on preprocessing, feature extraction and classification stage specifically for recognizing individual identity for biometrics trait using finger-vein are discussed.
Abstract: Biometrics trait using finger-vein has attracted numerous attention from researchers all over the world since the last decade. Various approaches have been proposed in regard to improving the accuracy of identification result. This paper discusses on the approaches taken from other researches on preprocessing, feature extraction and classification stage specifically for recognizing individual identity. The strengths and weaknesses of these approaches are critically reviewed. The classification approach using machine learning method is highlighted to determine the future direction and to fill the research gap in this field.
53 citations
TL;DR: Offline signature verification is a challenging pattern recognition task where a writer model is inferred using only a small number of genuine signatures using a combination of complementary writer mode and reader mode.
36 citations
Cites background from "Handwritten signature identificatio..."
..., 2009), and basic concepts of graph theory (Fotak et al., 2011)....
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29 Nov 2016
TL;DR: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Patternrecognition (SSPR), and S+S SPR 2016: Structural, Syntactic, and Statistical pattern recognition.
Abstract: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR). S+SSPR 2016: Structural, Syntactic, and Statistical Pattern Recognition pp. 553-563.
34 citations
Cites background from "Handwritten signature identificatio..."
...Recently, graphs have gained some attention in the field of handwritten document analysis [4] like for instance handwriting recognition [6], keyword spotting [7–9], or signature verification [10,11]....
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17 Aug 2018
TL;DR: This work proposes to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks, and demonstrates that combining the structural and statistical models leads to significant improvements in performance.
Abstract: Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties.
17 citations
01 Nov 2017
TL;DR: A novel structural approach to offline signature verification using an efficient cubic-time approximation of graph edit distance is introduced and several ways of creating, normalizing, and comparing signature graphs built from keypoints are put forward.
Abstract: Graphs provide a powerful representation formalism for handwritten signatures, capturing local properties as well as their relations. Yet, although introduced early for signature verification, only a few current systems rely on graph-based representations. A possible reason is the high computational complexity involved for matching two general graphs. In this paper, we introduce a novel structural approach to offline signature verification using an efficient cubic-time approximation of graph edit distance. We put forward several ways of creating, normalizing, and comparing signature graphs built from keypoints and investigate their performance on three benchmark datasets. The experiments demonstrate a promising performance of the proposed structural approach when compared with the state of the art.
16 citations
Cites background from "Handwritten signature identificatio..."
...The most prominent examples include [6] where signatures were represented with stroke primitives, [7] which proposed a modular graph matching approach, and [8] which leveraged some basic concepts of graph theory for signature verification....
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References
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TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
4,678 citations
"Handwritten signature identificatio..." refers background in this paper
...Key-Words: - handwritten signature, signature recognition, identification, graph theory, biometrics, behavioral characteristics...
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TL;DR: 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 using a mapping from the handwriting domain to the signature domain.
Abstract: 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.
343 citations
"Handwritten signature identificatio..." refers methods in this paper
...Since the approach presented later in this paper can be implemented as both off-line and on-line system we will cover previous work of the off-line and online handwritten identification systems....
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TL;DR: This paper attempts to eliminate the assumption that the written text is fixed by presenting a novel algorithm for automatic text-independent writer identification by taking a global approach based on texture analysis, where each writer's handwriting is regarded as a different texture.
341 citations
"Handwritten signature identificatio..." refers methods in this paper
...…achievements in the field of ISSN: 1790-5052 118 Issue 4, Volume 7, October 2011 the handwriting recognition and writer identification can be very important for the handwritten signature identification because all the methods developed in this field can be implemented to identify signature....
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01 Sep 2000
TL;DR: A new method to identify the writer of Chinese handwritten documents by taking the handwriting as an image containing some special texture, and writer identification is regarded as texture identification, which is a content independent method.
Abstract: In this paper, we describe a new method to identify the writer of Chinese handwritten documents. There are many methods for signature verification or writer identification, but most of them require segmentation or connected component analysis. They are content dependent identification methods, as signature verification requires the writer to write the same text (e.g. his name). In our new method, we take the handwriting as an image containing some special texture, and writer identification is regarded as texture identification. This is a content independent method. We apply the well-established 2D Gabor filtering technique to extract features of such textures and a weighted Euclidean distance classifier to fulfil the identification task. Experiments are made using Chinese handwritings from 17 different people and very promising results were achieved.
172 citations
"Handwritten signature identificatio..." refers methods in this paper
...This is why we will mention some of the previous work in this field as a good idea that can be used in signature identification....
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TL;DR: Both Bayesian classifiers and neural networks are employed to test the efficiency of the proposed feature and the achieved identification success using a long word exceeds 95%.
166 citations
"Handwritten signature identificatio..." refers methods in this paper
...This is why we will mention some of the previous work in this field as a good idea that can be used in signature identification....
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