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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Dissertation•
16 Jun 2015
TL;DR: In this article, the authors used two-dimensional photographs of people's faces to detect children in images and used the ratios of Euclidean distances between those landmarks for age estimation.
Abstract: Classification of face images can be done in various ways. This research uses two-dimensional photographs of people's faces to detect children in images. Algorithm for classification of images into children and adults is developed and existing algorithms are analysed. This algorithm will also be used for age estimation. Through analysis of the state of the art researchon facial landmarks for age estimationand combination with changes that occur in human face morphology during growth and aging, facial landmarks needed for age classification and estimation of humans are identified. Algorithm is based on ratios of Euclidean distances between those landmarks. Based on these ratios, children can be detected and age can be estimated.
8 citations
01 Sep 2019
TL;DR: This paper proposes to match the underlying graphs from different local perspectives and combine the resulting assignments by means of Dynamic Time Warping and demonstrates that the proposed approach outperforms state-of-the-art methods with respect to both accuracy and runtime.
Abstract: In recent years, different approaches for handwriting recognition that are based on graph representations have been proposed (e.g. graph-based keyword spotting or signature verification). This trend is mostly due to the availability of novel fast graph matching algorithms, as well as the inherent flexibility and expressivity of graph data structures when compared to vectorial representations. That is, graphs are able to directly adapt their size and structure to the size and complexity of the respective handwritten entities. However, the vast majority of the proposed approaches match the graphs from a global perspective only. In the present paper, we propose to match the underlying graphs from different local perspectives and combine the resulting assignments by means of Dynamic Time Warping. Moreover, we show that the proposed approach can be readily combined with global matchings. In an experimental evaluation, we employ the novel method in a signature verification scenario on two widely used benchmark datasets. On both datasets, we empirically confirm that the proposed approach outperforms state-of-the-art methods with respect to both accuracy and runtime.
8 citations
10 May 2019
TL;DR: An improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions is presented and it is explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.
Abstract: In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person’s signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.
7 citations
Cites background from "Handwritten signature identificatio..."
...It is well known that every person’s signature is unique in terms of its behavioral property and this fact has yielded a great community acceptance of its use as biometric for identification and authentication[5, 6]....
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25 May 2015
TL;DR: This daily based biometric characteristic, written signature, is used for identification and classification of students' papers and various exam documents used at University of Mostar.
Abstract: Handwritten signature is used in various applications on daily basis. Whether one signs a contract, work documents, petition, or wants to approve a check payment, one will use personal signature to do all those things. In this paper we use this daily based biometric characteristic for identification and classification of students' papers and various exam documents used at University of Mostar. In this paper we used OpenCV library as an image processing tool for feature extraction. As regards to classification method, we used Support Vector Machine.
7 citations
27 Jun 2012
TL;DR: Researchers in the field of biometrics found that human hand contains some characteristics that can be used for personal identification, including hand geometry and hand shape, which makes them suitable for development of various acquisition and authentication methods.
Abstract: Researchers in the field of biometrics found that human hand contains some characteristics that can be used for personal identification. Some of these are hand geometry and hand shape. These biometric characteristics are more than 30 years old and are very popular and widely accepted among people. That makes them suitable for development of various acquisition and authentication methods. So far, many contact-based and contact-less systems have been developed in the academy and commercial sector. Contact-less biometrics is becoming more important in this field, thus making the researchers to concentrate their efforts in this direction.
7 citations
Cites methods from "Handwritten signature identificatio..."
...Using basic graph theory concepts in the handwritten signature identification gave us promising results in this field as mentioned in [9]....
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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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