skilled forgeries only and when forgery word is used then it should be noted here that it means the skilled forgery. Signature Characteristics Signature must be treated as an image because a person may use any symbol or letter or group of letters etc. (according to the choice) as a signature, so it may be possible that one can't separate the letters written or even can't understand what is written. So, the system used for analysis of signature must use the concepts of image processing. Most probable, it is possible that the signature of a signer varied for every sign but there must be some unique characteristic to identify the signature so that it can be used as biometrics. Some essential characteristics are listed below: 
a) **Invariant: It should be constant over a long period of time.** 
b) **Singular: It must be unique to the individual.** c) **Inimitable: It must be irreproducible by other means.** 
d) **Reducible and comparable: It should be capable of being reduced to a format that is easy to** 
handle and digitally comparable to others [2]. 

As signature also belongs to one of the parameters which satisfy the above characteristics so it may be use as a proof of identification of a person. 

Types of signature verification As stated above signature is nothing but a pattern of special arrangement of pixels (i.e. an image). Still, we can classify the signature verification system based on whether only the image of a signature is available or the individual is personally signing before the verification system. Based on this, broadly, signature verification methods can be classified as: 

## A) **On-Line Or Dynamic Signature Verification System** B) **Off-Line Or Static Signature Verification System**

In off-line verification system, only static features are considered which rely purely on the signature's image but require less hardware. Whereas in case of on-line systems, dynamic feature are taken into consideration, which include the time when stylus is in and out of the contact with the paper, the total time taken to make signature and the position where the stylus is raised from and lowered onto the paper, number of break points, maximum/minimum pressure of stylus contact, speed etc [2], [3]. 

## Related Work

The 1st Signature verification system developed in 1965 but use of neural network for verification has been started in decades of 90s. Reena Bajaj et. al worked on signature verification using multiple neural classifiers. The authors have used 3 different types of global features projection moments, upper and lower envelop based characteristics with feed forward neural net for verification purpose [12]. Emre et. al had used global, direction and grid features and SVM (Support Vector Machine) as classification method. 

Authors also compared the performance using SVM and ANN [13]. Fixed point arithmetic with different classifiers such as HMM (Hidden Markov Models), SVM and ED (Euclidean Distance Classifier) 
analysed. Fathi et. al have used conjugate gradient neural network with string distance (SD) as local feature [16]. Enhanced modified directional feature and neural based classifiers have been used by Armand et. al [9]. Siddiqi et. al extracted chain code based features used ROC (Receiver Operating Characteristic) curve for classification [8]. Image registration, image fusion & DWT (Discrete Wavelet Transforms) are also used with Euclidean Distance Method for identification and verification of signatures. H. N. Prakash & D. S. Guru presented a new approach based on score level fusion for offline signature verification [6]. Authors have used distance & orientation features to form bi-interval valued symbolic representation of signature. They compare the extracted features for classification. 

Author also proposed both the simple energy density method [17] and chain code method [18] for offline signature verification system in 2010. 

This paper proposes a system for off-line signature verification using a directional feature mixed with energy density features extracted locally and Feed Forward Back Propagation Neural Network used as a classifier. Aspect ratio is also included as a global feature in energy density method. 

## 2 Proposed Approach

There are three approaches used in this paper for feature extraction. First one is 'The Energy Density method' and the second is 'The Directional Feature Method'. Author used thinning algorithm because he database extracted from the contour obviously will acquire more memory as compared to the thinned image. Also a comparative statement between the simplest energy density method, Directional Feature method and obviously the proposed directional feature with energy density method is developed so that it may be clear that is it worth to improve the accuracy on the cost of memory and time? 