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.
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Posted Content•
TL;DR: This article presents two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models, and proposes improvements both in terms of computational time and accuracy.
Abstract: Graphs provide a powerful representation formalism that offers great promise to benefit tasks like handwritten signature verification. While most state-of-the-art approaches to signature verification rely on fixed-size representations, graphs are flexible in size and allow modeling local features as well as the global structure of the handwriting. In this article, we present two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models. We provide a comprehensive description of the methods, propose improvements both in terms of computational time and accuracy, and report experimental results for four benchmark datasets. The proposed methods achieve top results for several benchmarks, highlighting the potential of graph-based signature verification.
6 citations
Cites methods from "Handwritten signature identificatio..."
...to use basic concepts of graph theory [19]....
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06 Jul 2016
TL;DR: The practical need in metaheuristic algorithms for selecting the identification thresholds by comparison with the classic gradient method is shown and some of the proposed series of multi-function, compared with the single use Bayes classifier and neural network is showed.
Abstract: An approach to the integration of multiple methods of user authentication and example of multi-classifier Bayesian and neural network is presented. The approach offers to find the convolution of outputs from multiple classifiers based on the complementary functions and to carry out the selection of the identification thresholds for each of the users. A number of complementary functions that use fundamentally different mathematical functions is analyzed. It is shown the practical need in metaheuristic algorithms for selecting the identification thresholds by comparison with the classic gradient method. The effectiveness some of the proposed series of multi-function, compared with the single use Bayes classifier and neural network is showed.
6 citations
01 Oct 2016
TL;DR: Polar-scale normalization (PSN) is proposed to scale signature size and make it stable and provides the better performance, when compared with traditional normalization schemes including min-max, decimal, z-score and MAD normalizations.
Abstract: Offline handwritten signature is still widely used for person verification in financial and business transactions. Most research in offline handwritten signature at-tempts to improve feature extraction and classification for the better recognition rate. The deformation and unsteadiness of handwritten signatures, such as direction, declination, and size, are also the key factors sensitive to recognition rate. Therefore, this paper focuses on the pre-processing phase, which is an alternative way to improve the accuracy and to make such factors stable. This study is based on the hypothesis; a table signature size is able to boost up the recognition rate. Therefore, polar-scale normalization (PSN) is proposed to scale signature size and make it stable. In this method, the signature images are transformed into the polar coordinate system consisting of polar distance and angle, and then normalized by ‖norm‖. The normalized distance is certainly estimated by polar coordinate that helps reduce the deformed images. The 5,739-sample signature images with 150 classes are used to test in the experiment. PSN provide the better performance, when compared with traditional normalization schemes including min-max, decimal, z-score and MAD normalizations. The results reveal that the proposed method can improve the average recognition rate up to 98.39%.
4 citations
28 Feb 2012
TL;DR: Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition and was evaluated to be high effectiveness in offline signature recognition.
Abstract: In this work a new offline signature recognition system based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained vectors are calculated to construct a feature vector for each signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of the system several experiments are carried out. Offline signature database from signature verification competition (SVC) 2004 is used during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition. Keywords—Fractal Dimension, Offline Signature Recognition, Radon Transform, Support Vector Machine
4 citations
Cites background from "Handwritten signature identificatio..."
...In [6] the authors considered a set of geometric and topologic features to map a signature image into two strings of finite symbols....
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01 Sep 2017
TL;DR: The results explored that adaption of the blind or visual signing data collection protocols has impact on the recognition performance less critical than hitherto expected.
Abstract: The ubiquitous nature of our digital lifestyle raised many security issues including signature imitation and stealing of our identity. Therefore, there is a need for robust systems to verify or identify the signatory. In this paper, in contradistinction to other researchers working in signature biometrics, we investigate and explore the impact of blind and visual signing in signature biometrics for online signature identification. Experimental performance evaluation, using the publicly available SUSIG signature database, is carried out to provide some new and preliminary insights into the relationship between different practical factors, in particular clarifying the impact on identification performance of the blind and visual signing data collection protocols used to support the signature processing. Our results explored that adaption of the blind or visual signing data collection protocols has impact on the recognition performance less critical than hitherto expected.
3 citations
Cites background from "Handwritten signature identificatio..."
...However, there are only few attempts of signature identification [7]....
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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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