ISSN: 1813-0410

Author : Faris Khlebus, Sameerah


OFFLINE SIGNATURE VERIFICATION BASED ON USING NEURAL NETWORK CLASSIFICATION

Sameerah Faris Khlebus

journal of kerbala university, Volume 13, Issue 4, Pages 234-246

The verification of handwritten signatures is one of the oldest and the most popular biometric authentication methods in our society. In addition, the evolution of technology, the different ways of comparing and analyzing signatures became more and more sophisticated. Based on the acquisition process, the field is divided into on-line and off-line parts. In on-line signature verification, the whole process of signing is captured using some kind of an acquisition device, while the off line approach relies merely on the scanned images of signatures. This research, deals some of the many open questions in the off-line field. It provides off-line signature recognition and verification system which is based on image processing, new improved method for features extraction proposed and artificial neural network are both used to attend the objective designed for this research , Two separate sequential neural networks are designed; one for signature recognition, and another for verification (i.e. for detecting forgery). A recognition network controls verification network parameters, which are produced individually for every signature. The System overall performs is enough to signature recognition and verification sing standard and popular dataset, In order to demonstrate the practical applications of the results, a complete signature verification framework has been developed, which incorporates all the previously introduced algorithms. The results provided in this it aim to present a deeper analytical insight into the behavior of the verification system than the traditional artificial intelligence-based"approaches.