Vertical Off-line Signature Feature Block for Verification

Handwritten signature image is normally used as a mark of endorsement of written document. Signatures of the same person vary and they can be forged by imposters. Effective feature extraction algorithm is needed in off-line signature verification. Robust features capable of increases interpersonal variation and decreases intra personal variation are required. This work presents robust signature feature that can be used to build effective off-line signature verification system. Signature processing is performed and the preprocessed signature image is vertically divided into sixteen smaller image blocks through the center of gravity. Three features are extracted from these smaller image blocks. Feature vector is formed and are passed to Support Vector Machine (SVM) for training and classification. The proposed signature feature vector increases the accuracy of tested off-line signature verification syste

Online Signature Verification Using Energy, Angle and Directional Gradient Feature With Neural Network

Abstract: Signature used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. It is very important to have anonline computerized signature Verification system differentiate digital signature. Hand written signature used every day at various places (Bank, Office etc) for the authentication of a person, but a signature of a person may not be same at different time or it may be generated by some fraud way. So therobust system is required for verification of the signature. The signature verification can be done either online or offline, here we are using online signature verification network. In the proposed system the signatures is taking as a image by the signature pad and apply image processing technique before the feature extraction to make the system effective. The angle, energy and chain code features are used in this paper to differentiate the signature. Neural network is used as a classifier for this system. The studies of online signature verification are given in this paper

Features selection for offline handwritten signature verification: State of the art

This research comes out with an in-depth review of widely used techniques to handwritten signature verification based, feature selection techniques. The focus of this research is to explore best features selection criteria for signature verification to avoid forgery. This paper further present pros and cons of local and global features selection techniques, reported in the state of art. Experiments are conducted on benchmark databases for signature verification systems (GPDS). Results are tested using two standard protocols; GPDS and the program for rate estimation and feature selection. The current precision of the signature verification techniques reported in state of art are compared on benchmark database and possible solutions are suggested to improve the accuracy. As the equal error rate is an important factor for evaluating the signature verification's accuracy, the results show that the feature selection methods have successfully contributed toward efficient signature verification