
## A Directional Feature With Energy Based Offline Signature Verification Network

Minal Tomar* **and Pratibha Singh**
Department of Electrical & Electronics* Malwa Institute of Technology Indore (M.P.) 452016 
(www.malwa-institute.com) 
Department of Electronics and Instrumentation Institute of Engineering and Technology Devi Ahilya Vishwavidyalaya, Indore (M.P.) 452017 
(www.iet.dauniv.ac.in) 
minal2121@rediffmail.com * 
prat_ibh_a@yahoo.com 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. So, it is very important to have a computerized signature verification system. In offline signature verification system dynamic features are not available obviously, but one can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. Author proposes a intelligent network used directional feature and energy density both as inputs to the same network and classifies the signature. Neural network is used as a classifier for this system. The results are compared with both the very basic energy density method and a simple directional feature method of offline signature verification system and this proposed new network is found very effective as compared to the above two methods, specially for less number of training samples, which can be implemented practically.

Key words: **Neural Network, Directional Feature, Energy Density, Neuron, Back propagation, FAR,** 
FRR 

## 1 Introduction

For human identification, the usage of biometrics is obvious and important in daily routine. Signature may be used as a biometrics as every signature is distinct. As signature has already used and accepted as an identification of the person who signed in so many systems, it is important to keenly observe the signature before having any conclusion about the signee. This gives rise to a computerized signature verification system. But, in many systems only image of a signature is available so offline signature verification seems more important than online signature verification system. The main problem is that a signature of a person may vary according to his/her mood, health etc. even the genuine signer may not copy his/her signature as it is. There will always a change observed. Then it seems somewhat difficult to distinguish between genuine signature and a forgery. 

With reference to this, it becomes a subject of continuous research until a purely faithful system is found to rely on. This paper is like one of a small step forward towards achieving the goal of a developed system for signature verification. Before proceeding further it is important to know about different type of forgeries. Forgeries can be classified as [1]: 
a) **Random forgery: It produces without any knowledge of signature shape or even the signer's** 
name. 

b) **Simple forgery: It is formed by knowing only the name of the signer's but without having any** 
examples of signer's signature style. 

c) **Skilled forgery: It is produced by looking at an original signature sample, attempting to imitate it** 
as closely as possible. 

This paper is developed with the mind set that random and simple forgeries is very easy to detect and refuse but the main issue is to caught the skilled forger. That's why; the complete paper is processed with 