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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01 Aug 2018
TL;DR: This paper investigates two recently presented structural methods for handwriting analysis: keypoint graphs with approximate graph edit distance and inkball models and proposes a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.
Abstract: For handwritten signature verification, signature images are typically represented with fixed-sized feature vectors capturing local and global properties of the handwriting. Graph-based representations offer a promising alternative, as they are flexible in size and model the global structure of the handwriting. However, they are only rarely used for signature verification, which may be due to the high computational complexity involved when matching two graphs. In this paper, we take a closer look at two recently presented structural methods for handwriting analysis, for which efficient matching methods are available: keypoint graphs with approximate graph edit distance and inkball models. Inkball models, in particular, have never been used for signature verification before. We investigate both approaches individually and propose a combined verification system, which demonstrates an excellent performance on the MCYT and GPDS benchmark data sets when compared with the state of the art.
15 citations
14 Jun 2020
TL;DR: Inspired by the recent use of image visibility graphs for mapping images into networks, their use as a parameter free, agnostic representation for exploring global as well as local information is introduced for the first time in offline SV literature.
Abstract: In spite of the overwhelming high-tech marvels and applications that rule our digital lives, the use of the handwritten signature is still recognized worldwide in government, personal and legal entities to be the most important behavioral biometric trait. A number of notable research approaches provide advanced results up to a certain point which allow us to assert with confidence that the performance attained by signature verification (SV) systems is comparable to those provided by any other biometric modality. Up to now, the mainstream trend for offline SV is shared between standard -or handcrafted- feature extraction methods and popular machine learning techniques, with typical examples ranging from sparse representation to Deep Learning. Recent progress in graph mining algorithms provide us with the prospect to re-evaluate the opportunity of utilizing graph representations by exploring corresponding graph features for offline SV. In this paper, inspired by the recent use of image visibility graphs for mapping images into networks, we introduce for the first time in offline SV literature their use as a parameter free, agnostic representation for exploring global as well as local information. Global properties of the sparsely located content of the shape of the signature image are encoded with topological information of the whole graph. In addition, local pixel patches are encoded by sequential visibility motifs-subgraphs of size four, to a low six dimensional motif profile vector. A number of pooling functions operate on the motif codes in a spatial pyramid context in order to create the final feature vector. The effectiveness of the proposed method is evaluated with the use of two popular datasets. The local visibility graph features are considered to be highly informative for SV; this is sustained by the corresponding results which are at least comparable with other classic state-of-the-art approaches.
12 citations
Cites background from "Handwritten signature identificatio..."
...In the literature one may find a limited number of research efforts which study graphs for offline SV [50-53]....
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01 May 2017
TL;DR: This paper reviews the various method of feature extraction in finger vein recognition and is functionally described and compared in three parts i.e., Finger vein image acquisition, pre-processing and feature extraction.
Abstract: In today's era, personal information security is major topic of concern. In this regard many advanced techniques are used but still in all of those biometric is most reliable. Biometric technologies are based on individual's biological and behavioural characteristics. These system includes human finger, vein, iris, hand and many other as its identifiers. Biometric system using finger-vein as one of its trait is most widely accepted. This paper reviews the various method of feature extraction in finger vein recognition. Most of the existing work is functionally described and compared in three parts i.e., Finger vein image acquisition, pre-processing and feature extraction.
11 citations
TL;DR: A novel procedure for online signature verification and recognition based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) is presented, and favorable experimental results confirm the effectiveness of the presented method in both online signature verify and recognition objects.
Abstract: Background: With the increasing advancement of technology, it is necessary to develop more accurate, convenient, and cost-effective security systems. Handwriting signature, as one of the most popular and applicable biometrics, is widely used to register ownership in banking systems, including checks, as well as in administrative and financial applications in everyday life, all over the world. Automatic signature verification and recognition systems, especially in the case of online signatures, are potentially the most powerful and publicly accepted means for personal authentication. Methods: In this article, a novel procedure for online signature verification and recognition has been presented based on Dual-Tree Complex Wavelet Packet Transform (DT-CWPT). Results: In the presented method, three-level decomposition of DT-CWPT has been computed for three time signals of dynamic information including horizontal and vertical positions in addition to the pressure signal. Then, in order to make feature vector corresponding to each signature, log energy entropy measures have been computed for each subband of DT-CWPT decomposition. Finally, to classify the query signature, three classifiers including k-nearest neighbor, support vector machine, and Kolmogorov–Smirnov test have been examined. Experiments have been conducted using three benchmark datasets: SVC2004, MCYT-100, as two Latin online signature datasets, and NDSD as a Persian signature dataset. Conclusion: Obtained favorable experimental results, in comparison with literature, confirm the effectiveness of the presented method in both online signature verification and recognition objects.
10 citations
Cites methods from "Handwritten signature identificatio..."
...Concepts of graph theory have been used for online signature identification in the study byFotak et al.[36] A fast classification using graph norm and comparison between each signature graph concepts value with the saved values in the dataset has been conducted to obtain an identification accuracy of 94....
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Journal Article•
TL;DR: This paper presents three different approaches for detection of a signature’s skew angle and explains how a slant angle can be detected and corrected.
Abstract: For any signature verification system, detection and correction of skew and slant angles are two important factors in preprocessing stage. To make an efficient verification system, the input signature image should be perfectly preprocessed as far as possible so that we can properly extract the required features. In correction of skew angle, attempt is made that word orientation of the signature is parallel to horizontal direction. On the other hand, in slant angle correction, operations are performed to make all the vertical strokes erect [1]. This paper presents three different approaches for detection of a signature’s skew angle. Also it is explained how a slant angle can be detected and corrected.
9 citations
Cites background from "Handwritten signature identificatio..."
...Amount of skew and slant may depend on different factors like writing surface and material, physical and psychological state of a person and environmental factors etc [2]....
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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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