Signature Verification using Normalized Static Features and Neural Network Classification

Signature verification is very widely used in verification of the identity of any person. Now a days other biometric verification system has been evolved very widely like figure print, iris etc., but signature verification through computer system is still in development phase. The verification system is either through offline mode or online mode in online systems the dynamic information of a signature captured at the time the signature is made while in offline systems based on the scanned image of a signature. In this paper, a method is presented for Offline signatures Verification, for this verification system signature image is first pre-processed and converted into binary image of same size with 200x200 Pixels and then different features are extracted from the image like Eccentricity, Kurtosis, Skewness etc. and that features are used to train the neural network using back-propagation technique. For this verification system 6 different user signatures are taken to make database of the feature and results are analysed. The result demonstrate the efficiency of the proposed methodology when compared with other existing studies. The proposed algorithm gives False Acceptance Rate (FAR) as 5.05% and False Rejection rate (FRR) as 4.25%

Revisión de algoritmos de verificación automática de firmas off-line

Nowadays, the signature is one of the most accepted badges for personal identification. Its inclusion is mandatory in documents such as bank checks, contracts, credit cards, among other public and private documents. However, the signature has become an attractive target for counterfeiting and, consequently, for fraud. For this reason, research has been carried out on automated signature recognition and state-of-the-art studies that are now required to be updated, since the most comprehensive work dates back to 2008 and in the following years further research has been carried out. The present work focuses on the comparative study of verification techniques of signatures offline from the point of view of efficiency and accuracy to verify the person’s authenticity. The research methodology used considers the procedure proposed by Kitchenham, which has been adapted, and involves the phases of planning, development and reporting of the review.En la actualidad, la firma es uno de los distintivos más aceptados para la identificación personal. Su inclusiónes obligatoria en documentos como cheques bancarios, contratos, tarjetas de crédito, entre otros documentos públicos y privados. No obstante, la firma se ha convertido en un atractivo objetivo para las falsificaciones y, en consecuencia, para el fraude. Por esta razón, se han realizado investigaciones en soluciones automatizadas de reconocimiento de firmas y estudios del estado del arte que ahora resulta necesario actualizar, puesto que el trabajo más exhaustivo data del 2008 y en los años siguientes se han efectuado nuevas investigaciones. El presente trabajo se enfoca en el estudio comparativo de las técnicas de verificación de firmas off-line desde los puntos de vista de eficiencia y exactitud para verificar la autenticidad de la persona. La metodología de investigación utilizada considera el procedimiento propuesto por Kitchenham, el cual ha sido adaptado e involucra las fases de planeamiento, desarrollo y reportes de la revisión. &nbsp

Off-line verification technique for Hindi signatures

Handwritten signature is one of the oldest biometric attributes used for authentication of an individual or a document. The purpose of this study is to present an empirical contribution towards the understanding of signature verification using a novel method involving off-line Hindi (Devnagari) signatures. Although research in the field of signature verification involving Western signatures has been well studied, there has been relatively little attention devoted to non-Western signatures such as Chinese, Japanese, Arabic, Persian etc. In this study, the performance of an off-line signature verification system involving Hindi signatures, whose style is distinct from Western scripts, was investigated. The gradient feature, Zernike moment features and SVMs were considered for verification. To the best of the authors' knowledge, Hindi signatures investigated as part of a large dataset have never been used for the task of signature verification, and this research work is only the second important report using Hindi signatures in this area. An encouraging accuracy of 90.69% was obtained using gradient feature. The Hindi signature database employed for experimentation consisted of 2400 (100 × 24) genuine signatures and 3000 (100 × 30) forgeries. The error rates of 11.50% FRR and 7.12% FAR were obtained through experimentation using gradient features. © The Institution of Engineering and Technology 2013