International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD) ISSN 2249-684X Vol. 3, Issue 4, Oct 2013, 1-12 © TJPRC Pvt. Ltd. OFFLINE SIGNATURE VERIFICATION USING STATISTICAL FEATURE DEVSHRI SATYARTHI & R. K. GUPTA Department of CSE & IT, M.I.T.S., Gwalior, Madhya Pradesh, India ABSTRACT A method for offline (static) handwritten signature identification and verification proposed based on a combination of different statistical measures. In the proposed method focus is on the combination of statistical measures in order to improve the overall efficiency as individual measure lack in providing unique feature for different signature and provide generalized feature for minute change in signature of the same person. The proposed method is tested our own signature database contains 400 offline signature of individuals including, 1 test signature and the result are compare with other state of art of method sand prove that proposed methods is better in terms of efficiency. KEYWORDS: Signature Verification, Statistical Feature, Threshold, Euclidean Distance INTRODUCTION Signature is composed of special characters and brandish and therefore most of the time they can be unreadable. Signature are the primary mechanism both authentication and authorization in legal document and transaction, so need for research scope of person authentication has increased in upcoming year. Offline signature based on only pixel image can be evaluated. Offline signature arrest only one time writing process, in which all information available in static images. One most important is offline has the advantage of using it in the same way as the existing manual recognition method. Recognition and Verification are most important method to finding the signature is genuine or forgery. Recognition means to find the identification of the signature owner, and Verification means taking a decision about whether the signature is genuine or forgery. In this paper focus on the offline signature verification to based on statistical analysis. In paper take three type signatures there are: (i) random, (ii) simple, (iii) skilled. Random forgeries are written by that person who doesn‟t know the shape of original signature. Simple forgeries are written by a person who knows the shape of original signature without much practice. Skilled forgeries are written by a person who knows the shape of original signature without much practice, and forgeries are four types there are: (a) genuine signature, (b) random forger, (c) simulated simple forgery, and (d) simulated skilled forgery. Genuine signature is original signature, random signature is something learn but not original signature, simulated simple forgery is copy not know original shape of signature without any practice, and simulated skilled forgery is copy not know original shape of signature with need practice. QUALITY PERFORMANCE MEASURES In evaluating the performance of a signature verification system, there are two important factors: the false rejection rate (FRR) of genuine signatures and the false acceptance rate (FAR) of forgery signatures and these two are inversely related. The false rejection rate (FRR), the false acceptance rate (FAR), the equal error rate (EER) and the average rate (AER) are used as quality performance measures.