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

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

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

Feature selection method for offline signature verification

Signature verification is defined as one of the biometric identification method using a person’s signature characteristics. The task of verifying the genuineness of a person signature is a complex problem due to the inconsistencies in the person signatures such as slant, strokes, alignment, etc. Too many features may decrease the False Rejection Rate (FRR) but also increases the False Acceptance Rate (FAR). A low value of FAR and FRR are required to obtain accurate verification result. There is a need to select the best features set of the signatures attributes among them. A combination of the current global features with four new features will be proposed such as horizontal distance, vertical distance, hypotenuse distance and angle. However, the value of FAR may increase if too many features are used which result a slow verification performance. In order to select the best features, the difference between the mean of the standard deviation ratio of each feature will be used. The main objective is to increase the accuracy of verification rate. This can be determined using best features set selected during the features selection process. A selection of signature set with strong feature sets will be used as a control parameter. The parameter is then used to validate the results

An offline writer independent signature verification method with robustness against scalings and rotations

Handwritten signatures are still one of the most used and accepted methods for user au thentication. They are used in a wide range of human daily tasks, including applications from banking to legal processes. The signature verification problem consists of verifying whether a given handwritten signature was generated by a particular person, by com paring it (directly or indirectly) to genuine signatures from that person. In this research work, a new offline writer-independent signature verification method is introduced (named VerSig-R), based on a combination of handcrafted Moving Least-Squares features and features transferred from a convolutional neural network. In our experiments, VerSig-R outperforms state-of-the-art techniques on Western-style signatures (CEDAR dataset), while also obtaining competitive results on South Asian-style handwriting (Bangla and Hindi datasets). Furthermore, a wide range of experiments demonstrate that VerSig-R is the most robust in relation to differences in scale and rotation of the signature images. This work also presents a discussion on dataset bias and on cross-dataset performance of VerSig-R, as well as a small user study showing that the proposed technique outperforms the expected human accuracy on the signature-verification task. Finally, a discussion on the impact of the number of signature examples (per writer) used during training on performance and execution time is presented

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