Signature Verification through Pattern Recognition

The signature is being used as a personal authentication this fact forces the need for an automatic verification system. The verification can be done either Offline or Online based on the application that is to be prepared. The Online systems use dynamic characteristics of a signature captured at the time the signature is made. While Offline systems work on the scanned image of a signature.[4[We have worked on the Offline Verification of signatures using a set of shape based geometric characteristics. Features that are used are Baseline Slant Angle, Aspect Ratio, Normalized Area, Center of Gravity, number of edge points, number of cross points, and the Slope of the line joining the Centers of Gravity of two halves of a signature scanned image. Pre-processing of a scanned image is necessary to differentiate the signature part and to remove any spurious noise present, before extracting the features. [4] The system is initially trained using a database of signatures acquired from those individuals whose signatures have to be authenticated by the system. For each subject a average signature is obtained integrating the above features derived from a set of his/her genuine sample signatures. This average signature acts as the basis for verification against a claimed test signature. Euclidian distance in the feature space between the claimed signature and the template serves as a measure of similarity between the two. If this distance is less than a pre-defined threshold (corresponding to minimum acceptable degree of similarity), the test signature is verified to be that of the claimed subject else detected as a forgery.[4] The details of pre-processing as well as the features depicted above are described in the report along with the implementation details and simulation results.[4

Signature Verification Approach using Fusion of Hybrid Texture Features

In this paper, a writer-dependent signature verification method is proposed. Two different types of texture features, namely Wavelet and Local Quantized Patterns (LQP) features, are employed to extract two kinds of transform and statistical based information from signature images. For each writer two separate one-class support vector machines (SVMs) corresponding to each set of LQP and Wavelet features are trained to obtain two different authenticity scores for a given signature. Finally, a score level classifier fusion method is used to integrate the scores obtained from the two one-class SVMs to achieve the verification score. In the proposed method only genuine signatures are used to train the one-class SVMs. The proposed signature verification method has been tested using four different publicly available datasets and the results demonstrate the generality of the proposed method. The proposed system outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio

Determining the Personal identity based on Handwriting as a Biometric identification

This paper describes methods for off-line identification of the writer based on handwriting features. Different methods for extracting and combining features are reported in the literature for pattern recognition purposes. Many aspects have influence over the writer identification such as: symmetry, slant angle, percent of black and white pixels, height/width ratio of the letters, direction of the base line, the position of the horizontal and vertical lines in the segments, histograms, contour profiles, spots, etc. The method creates a feature-vector associated with a writing manner of an individual and computes the correlation measure to express the similarity with the previously stored handwritten samples of the Cyrillic letters. The presented system is based on image processing and pattern recognition methods. The approach analyses the handwriting as an image-texture, it is content independent and uses feature set based on the global statistical, structural and topological characteristics. An experiment was performed to discover the most reliable features that contribute to the identification of the writer. Handwritten biometric identification is applicable in many areas such as: security systems, forensics, financial etc

Новый признак для описания изображений рукописной подписи на базе локальных бинарных шаблонов

Objectives. The problem of describing the invariant features of a digital image of handwritten signature that describes the distribution of its local features is considered. The formation of fundamentally new approach to the calculation of such features is described.Methods. Digital image processing methods are used. First an image is converted into a binary representation, then its morphological and median filtering is performed. Then using the method of principal components, the image is rotated to give the signature a horizontal orientation. A rectangle describing the signature is cut out, then it is scaled to the template of a certain size. In the article the template of 300×150 pixels was used. Then the border of the signature is formed. Local binary patterns are calculated from its binary contour, i.e. each pixel is assigned a number from 0 to 255, which describes the location of the edge pixels in 3×3 neighborhood of each pixel. A histogram of calculated patterns for 256 intervals is formed. The first and last intervals are discarded because they correspond to all black and white pixels in the neighborhood and are not informative. The remaining 254 numbers of the array form new local features of the signature.Results. The studies were performed on the bases of digitized signatures TUIT and CEDAR containing true and fake signatures of 80 persons. The accuracy of correct verification of signatures on these bases was about 78 % and 70 %.Conclusion. The possibility of using the proposed possibilities for solving the problems of verifying the authenticity of handwritten signatures has been experimentally confirmed.Цели. Рассматривается задача описания инвариантных признаков цифрового изображения рукописной подписи, представляющих распределение ее локальных особенностей. Подробно описывается формирование принципиально нового подхода к вычислению таких признаков.Методы. Используются методы обработки цифровых изображений. Сначала изображение преобразуется в бинарное представление, затем выполняется его морфологическая и медианная фильтрация. Далее с помощью метода главных компонент осуществляется поворот изображения для придания подписи горизонтальной ориентации. Вырезается описывающий подпись прямоугольник и масштабируется в шаблон определенного размера (в статье использовался шаблон размером 300×150 пикселов). После этого формируется граница подписи. По ее бинарному контуру вычисляются локальные бинарные шаблоны, т. е. каждому пикселу ставится в соответствие число от 0 до 255, которое описывает расположение контурных пикселов в окрестности 3×3 каждого пиксела. Формируется гистограмма вычисленных шаблонов для 256 интервалов. Первый и последний интервалы отбрасываются, так как они соответствуют всем черным и белым пикселам в окрестности и не являются информативными. Оставшиеся 254 числа представляют собой массив новых локальных признаков подписи.Результаты. Исследования выполнены на базах оцифрованных подписей TUIT и CEDAR, содержащих истинные и поддельные подписи 80 человек. Точность корректной верификации подписей на этих базах составила порядка 78 и 70 %.Заключение. Экспериментально подтверждена возможность применения предложенного признака для решения задач верификации подлинности рукописной подписи

Penganalisa Jaringan Hotspot Berbasis Support Vector Machine

Penelitian ini membahas penganalisa jaringan menggunakan Support Vector Machine (SVM) setelah pada penelitian sebelumnya menggunakan Jaringan Syaraf Tiruan (JST). Ada banyak aplikasi yang tersedia di pasaran saat ini untuk memberikan grafik jaringan area hotspot kita. Grafik ini akan dianalisis oleh administrator jaringan. Karena area hotspot sering berjalan 24 jam, administrator mengalami kesulitan untuk memonitor lalu lintas setiap saat. Oleh karena itu kami merancang sistem otomatis untuk membantu administrator jaringan dalam memantau jaringan. Sistem ini akan menggantikan keterampilan manusia dalam menafsirkan grafik dengan SVM. Untuk meminimalkan jumlah vektor masukan kita menggunakan nilai rata-rata dari sumbu, sehingga misalnya mikro-komputer notebook, laptop, PDA, dan gadget lainnya dapat menangani sistem ini. Hasil pengujian menunjukkan sistem ini dapat mengklasifikasikan antara normal, lalu lintas tinggi dan un- normal grafik jaringan secara berkala. Kata Kunci: Penganalisa Jaringan, Support Vector Machine, Hotspot We present a Support Vector Machine-based network analyzer system for hotspot area as the extension of previous research that using Neural Networks (NNs). There are many applications that available in the market today for providing us the network graph of our hotspot areas. These graphs will be analyzed by a network administrator. Because a hotspot area often runs 24 hours, an administrator has a difficulty to monitor the traffic all the time. Therefore we proposed the automatic system to help a network administrator in monitoring the network. This system will replace human skill in interpreting the graph with a Support Vector Machine System. To minimize the number of input vector we use mean value of axis, so the micro-computer e.g. notebook, laptop, PDA, and other gadgets can handle this system. Testing result showed this system could classify between normal, high and un-normal traffic of network graph periodically.

Offline Handwritten Signature Verification - Literature Review

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.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017

Signature verification using grid based feature extraction

Signature does not depend on physical features like that of iris detection, gait, fingerprint, facial features; instead it’s a completely behavioural attribute of an individual. The field of signature verification is broadly classified into two parts i.e. online and offline. Online signature verification deals with signatures obtained from digital tablets or any such device where in addition to spatial features of the signature; time, pressure etc. information is also available. The sole purpose of this research paper is to develop an efficient signature authentication system which is still an important part of biometric identification methods

An Efficient Automated Attendance Entering System by Eliminating Counterfeit Signatures using Kolmogorov Smirnov Test

Maintaining the attendance database of thousands of students has become a tedious task in the universities in Sri Lanka This paper comprises of 3 phases signature extraction signature recognition and signature verification to automate the process We applied necessary image processing techniques and extracted useful features from each signature Support Vector Machine SVM multiclass Support Vector Machine and Kolmogorov Smirnov test is used to signature classification recognition and verification respectively The described method in this report represents an effective and accurate approach to automatic signature recognition and verification It is capable of matching classifying and verifying the test signatures with the database of 83 33 100 and 100 accuracy respectivel

Genuine Forgery Signature Detection using Radon Transform and K-Nearest Neighbour

Authentication is very much essential in managing security. In modern times, it is one in all priorities. With the advent of technology, dialogue with machines becomes automatic. As a result, the need for authentication for a variety of security purposes is rapidly increasing. For this reason, biometrics-based certification is gaining dramatic momentum. The proposed method describes an off-line Genuine/ Forgery signature classification system using radon transform and K-Nearest Neighbour classifier. Every signature features are extracted by radon transform and they are aligned to get the statistic information of his signature. To align the two signatures, the algorithm used is Extreme Points Warping. Many forged and genuine signatures are selected in K-Nearest Neighbour classifier training. By aligning the test signature with each and every reference signatures of the user, verification of test signature is done. Then the signature can be found whether it is genuine or forgery. A K-Nearest Neighbour is used for classification for the different datasets. The result determines how the proposed procedure is exceeds the current state-of-the-art technology. Approximately, the proposed system’s performance is 90 % in signature verification system