Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks

Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm

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

OSPCV: Off-line Signature Verification using Principal Component Variances

Signature verification system is always the most sought after biometric verification system. Being a behavioral biometric feature which can always be imitated, the researcher faces a challenge in designing such a system, which has to counter intrapersonal and interpersonal variations. The paper presents a comprehensive way of off-line signature verification based on two features namely, the pixel density and the centre of gravity distance. The data processing consists of two parallel processes namely Signature training and Test signature analysis. Signature training involves extraction of features from the samples of database and Test signature analysis involves extraction of features from test signature and it’s comparison with those of trained values from database. The features are analyzed using Principal Component Analysis (PCA). The proposed work provides a feasible result and a notable improvement over the existing systems

A Hybrid Features for Signature Recognition Using Neural Network

       In automatic personal recognition systems, biometric features is used as recognition measure based on biological traits such as face, iris, fingerprint, etc…or gait, signature which is considered behavioral characteristics. Signature verification is one of the authentication methods which can provide security at maintenance and low cost. The most essential and challenging stage of any off-line signature system is feature extraction stage. The accuracy and robust of the recognition system depends basically on the usefulness of the signature features extracted by this system. If the extracted features from a signature's image doesn't robust this will cause to higher verification error-rates especially for skilled forgeries in hacker the system. In this paper, we present a new offline handwritten signature recognition system based on combination of global with Statistical and GLCM (Grey Level Co-occurrence Matrix) features using neural network as classifier tool. The global, Statistical and GLCM features are combined to consist a vector of 14 features for the authentication of the signature. Verification of signatures is decided using neural network. The experimental results obtained by using a database of 7 individuals’ signatures. A total number of 70 images are collected with 10 signatures for each person, 5 of the signatures are used in training phase, and the remaining 5 signatures are used in testing phase. In this proposed method the results show 100% recognition accuracy for training and 97.1% recognition accuracy for the testing

A Hybrid Features for Signature Recognition Using Neural Network

       In automatic personal recognition systems, biometric features is used as recognition measure based on biological traits such as face, iris, fingerprint, et

Identity Theft Mitigation in Kenyan Financial Sectors (SACCOs): Handwritten Signature Verification

The existence of identity theft in society has become a major concern due to the effects it causes to those that are affected by it, more especially in the financial sector. Thus this thesis establishes the existence of identity theft issues in the financial sector loan sections and proposes an algorithm that addresses the mitigation processes of identity theft by having the signatures on the loan forms verified using the implementation of the proposed algorithm, then the results are compared with the human experts verification that are done on a daily basis. From the qualitative data collected from the four SACCOs presented indicate the 93% of the respondents knew that forgery of one’s signature in the SACCO exists and from the 93%, 95% of them had been victims of identity theft and 50% of them knew it after deductions were been made from their accounts. The algorithm was implemented in a prototype that was used to test the signatures that were corrected from various individuals that belonged to various SACCOs. The prototype had successfully verified 80.1% of the test signatures and as expected the highest results from the four Human experts verification of forged signature was 8.3% indicating that they had indicated more signatures as originals. The prototype thus recorded an accuracy of 91.4% and a precision of 60.0%.

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Automatic intrapersonal variability modeling for offline signature augmentation

Orientador: Luiz Eduardo Soares de OliveiraCoorientadores: Robert Sabourin e Alceu de Souza Britto Jr..Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 19/07/2021Inclui referências: p. 93-102Área de concentração: Ciência da ComputaçãoResumo: Normalmente, em um cenario do mundo real, poucas assinaturas estao disponiveis para treinar um sistema de verificacao automatica de assinaturas (SVAA). Para resolver esse problema, diversas abordagens para a duplicacao de assinaturas estaticas foram propostas ao longo dos anos. Essas abordagens geram novas amostras de assinaturas sinteticas aplicando algumas transformacoes na imagem original da assinatura. Algumas delas geram amostras realistas, especialmente o duplicator. Este metodo utiliza um conjunto de parametros para modelar o comportamento do escritor (variabilidade do escritor) ao assinar. No entanto, esses parametros so empiricamente definidos. Este tipo de abordagem pode ser demorado e pode selecionar parametros que nao descrevem a real variabilidade do escritor. A principal hipotese desse trabalho e que a variabilidade do escritor observada no dominio da imagem tambem pode ser transferido para o dominio de caracteristicas. Portanto, este trabalho propoe um novo metodo para modelar automaticamente a variabilidade do escritor para a posterior duplicacao de assinaturas no dominio de imagem (duplicator) e dominio de caracteristicas (filtro Gaussiano e variacao do metodo de Knop). Este trabalho tambem propoe um novo metodo de duplicacao de assinaturas estaticas, que gera as amostras sinteticas diretamente no dominio de caracteristicas usando um filtro Gaussiano. Alem disso, uma nova abordagem para avaliar a qualidade de amostras sinteticas no dominio de caracteristicas e apresentada. As limitacoes e vantagens de ambas as abordagens de duplicacao de assinaturas tambem sao exploradas. Alem de usar a nova abordagem para avaliar a qualidade das amostras, o desempenho de um SVAA e avaliado usando as amostras e tres bases de assinaturas estaticas bem conhecidas: a GPDS-300, a MCYT-75 e a CEDAR. Para a mais utilizada, GPDS-300, quando o classificador SVM foi treinando com somente uma assinatura genuina por escritor, ele obteve um Equal Error Rate (EER) de 5,71%. Quando o classificador tambem utilizou as amostras sinteticas geradas no dominio de imagem, o EER caiu para 1,08%. Quando o classificador foi treinado com as amostras geradas pelo filtro Gaussiano, o EER caiu para 1,04%.Abstract: Normally, in a real-world scenario, there are few signatures available to train an automatic signature verification system (ASVS). To address this issue, several offline signature duplication approaches have been proposed along the years. These approaches generate a new synthetic signature sample applying some transformations in the original signature image. Some of them generate realistic samples, specially the duplicator. This method uses a set of parameters to model the writer's behavior (writer variability) during the signing act. However, these parameters are empirically defined. This kind of approach can be time consuming and can select parameters that do not describe the real writer variability. The main hypothesis of this work is that the writer variability observed in the image space can be transferred to the feature space as well. Therefore, this work proposes a new method to automatically model the writer variability for further signature duplication in the image (duplicator) and the feature space (Gaussian filter and a variation of Knop's method). This work also proposes a new offline signature duplication method, which directly generates the synthetic samples in the feature space using a Gaussian filter. Furthermore, a new approach to assess the quality of the synthetic samples in the feature space is introduced. The limitations and advantages of both signature augmentation approaches are also explored. Despite using the new approach to assess the quality of the samples, the performance of an ASVS was assessed using them and three well-known offline signature datasets: GPDS-300, MCYT-75, and CEDAR. For the most used one, GPDS-300, when the SVM classifier was trained with only one genuine signature per writer, it achieved an Equal Error Rate (EER) of 5.71%. When the classifier also was trained with the synthetic samples generated in the image space, the EER dropped to 1.08%. When the classifier was trained using the synthetic samples generated by the Gaussian filter, the EER dropped to 1.04%