ABSTRACT

In this paper we present new improved off-line signature verification system using global and texture features of signatures. This scheme is based on the technique that applies preprocessing on the signature to get a binary image and then calculate the global and texture features points from it and maintain a feature vector. All calculations are done on the basis of these feature points. The feature vector obtained from the global and texture features is used to compare with the feature vector of incoming testing signature. Based on the values obtained, the network will decide the appropriateness of the signature. The suggested scheme discriminates between original and forged signatures using artificial neural network (ANN) for training and verification of signatures. The method takes care of simple and random forgeries and the skilled forgeries are also eliminated in greater extent. The objective of the work is to reduce two vital parameters, False Acceptance Rate (FAR) and False Rejection Rate (FRR). So the results are expressed in terms of FAR and FRR and subsequently comparative analysis has been made with standard existing techniques. Results obtained by our proposed algorithm are more efficient than most of the existing techniques.