International Journal of Advancements in Research & Technology, Volume 2, Issue 5, M ay-2013 383 ISSN 2278-7763 Copyright © 2013 SciResPub. IJOART REVIEW ON OFFLINE SIGNATURE VERIFICATION METHODS BASED ON ARTIFICIAL INTELLIGENCE TECHNIQUE 1 IT Department, ITM Bhilwara, Rajasthan, India; 2 I-Gate Global Solution Limited, Noida, U.P, India; 3 IT Department, ITM Bhilwara, Rajasthan, India ABSTRACT Signature can be 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 a computerized signature verification system. In case of offline signature verification system dynamic features are not included obviously, but we can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. Author wants to illustrate two techniques reviewed by him on Offline signature Verification. Those techniques are mixed of Energy with Angle and Energy with Chain Code. Keywords : Neural Network, Angle, Energy Density, Chain code, FAR and FRR. I. INTRODUCTION or person identification, the usage of biometrics is very general and important in daily routine. Signature can be used as a biometrics as every signature is distinct in nature. As signature has already taken and accepted as an identification of the person who signed in so many systems, it is very much important to keenly observe the signature before having any conclusion about the signee. This gives opportunity to develop a computerized signature verification system. But, in many cases only image of a signature is available so offline signature verification seems more important than online signature verification system. The main thing of consideration is that a signature of a person may vary according to the mood, health etc. even the genuine signer may not sign in the same manner as his/her signature as it is. A change is observed every time. Then this seems somewhat difficult to distinguish between genuine signature and a forgery. Basically, approaches to signature verification divided into two categories according to the acquisition of the data: On- line and Off-line. On-line signature records the motion of the stylus while the signature is produced, and includes position, and possibly velocity, acceleration and pressure of pen, as functions of time. Online signature verification systems use this information captured during acquisition. These dynamic characteristics are specific to each and every individual and sufficiently stable . Off-line signature is a 2-D image of the signature. Processing Off-line is complex due to the absence of dynamic characteristics. Difficulty also lies in the fact that it is hard to break signature strokes due to highly stylish and unconventional writing styles. The quality and the variety of the writing pen may also affect the nature of the signature obtained. The non-repetitive nature of variation of the signatures, because of the age, geographic location, illness, and perhaps the emotional state of the person, accentuates the problem. All these joined together, cause large intra-personal variation. A robust system has to be developed which should not only be able to consider these factors but also able to detect various types of forgeries. The systems should neither be too coarse nor too sensitive. It should have an acceptable trade-off between a low False Rejection Rate (FRR) and a low False Acceptance Rate (FAR). The proposed systems should also find an optimal storage and comparison solution for the extracted feature points. The problem is approached in two steps. Initially the scanned signature image is preprocessed to be suitable for extracting features. Then the preprocessed image is used to extract relevant geometric parameters that can distinguish signatures of different persons. The next step involves the use of these extracted features to verify a given image. A. Motivation The motivation behind the review is the growing need for a full proof signature verification scheme which can guarantee maximum possible security from fake signatures. The idea behind the review is also to ensure that the proposed scheme can provide comparable and if possible better performance than already established offline signature verification schemes. There may be a case where the type of verification system used for training differs from classification using network. Though the test sample is of a genuine person, it might not be possible to prove with either of these systems alone. B. Research Objectives IJOART Vivek Kr. Shrivastava Imran Hussain, Vikash Shrivastava,