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7/27/2019 1. Offline Signature.full

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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 withother 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 doesnt 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.

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2 Devshri Satyarthi & R. K. Gupta

The FRR is the ratio of genuine test signatures rejected to the total number of genuine test signatures submitted.

The FRR called the type I error and is defined as,

The FAR is the ratio of the number of forgeries accepted to the total number of forgeries submitted. The FAR is

also called the type II error and is defined as,

The average of the FRR and FAR is called the AER.

The EER is find the equal error rate between forgeries accepted and forgeries submitted number. The EER is also

called the type III error.

RELATED WORK

D. R. Kisku et al. focus on offline signature identification by fusion of three classifiers using SVM, there are

Mahalanobis distance, Euclidean distance and the Gaussian empirical rule because the use of these classifier is to reduce

the error rates in terms of skilled forgery detection with less computational complexity, and its depends on the quality of

the matching scores produced by individual matchers, and also overcome the problem of non-uniformity. So the main

reason to use the SVM to produce quality matching scores by fusing the individual matchers [1].

B. kovari, et al. provides statistical approach for modeling different kinds of signature verification system andestimating their performance limitations based on simple properties of the signature database, which is used to evaluate the

system. In this paper give the solution of address the problem of improvement and thereby possibly break the 5% barrier.

The paper based on SVM and feature extraction [2].

M. K. Randhawa focused on SVM and Hues invariant moment. The importance of using SVM is that it finds

hyper plane, which maximizes the distance from either class to hyper plane and distinguish the largest possible number of

point belonging to the same class on same side, which reduces the misclassification error of both training and testing set. It

uses offline signature pre-processing for image, including normalization, grey scales, skeltonisation and binarisation, and

based on these feature extraction technique based on fusion Hus moment invariants and Zone features are extracted. Thesemoments consider the image scaling, translation, rotation, and shear. In zone feature it divides the image into three equal

size horizontal rows called zone, and verify the signature using the SVM technique [3].

So it is used a statistical learning theory based linear SVM has been used for fusion of different sources of feature

sets which are often found better compared to other statistical methods of fusion. In the proposed SVM based fusion

scheme, matching score based fusion is adopted since at this level the fusion scheme is independent of classifiers used for

generating matching score. It use global and local feature extraction for determine the identity of authentic and forgery

signatures successfully from the database such as width, height, area of black pixels of each grid region, normalized area of

black pixels, center of gravity, aspect ratio, horizontal and vertical projections, etc. , according to these feature extraction

find the value as a input and on base of inputs and database perform the matching score and generate the result with the

help of measurement techniques for generating matching scores, such as Euclidean distance, Mahalanobis distance and

Gaussian empirical rule [3].

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Offline Signature Verification Using Statistical Feature 3

E. J. R. Justino et al. HMMs efficiency, it use cross-validation process for achieving best representative signature

model from the database. And the reason for use these method is that it define automatic generated threshold in order of

false accept rate (FAR) and false reject rate (FRR) signature [4].

E. Yacoubi et al. Use HMMs and the cross-validation principle for random forgery detection. A grid is

superimposed on each signature image, segmenting it into local square cells. From each cell, the pixel density is computed,

so that each pixel density represents a local feature. Each signature image is therefore represented by a sequence of feature

vectors, where each feature vector represents the pixel densities associated with a column of cells [7].

Y. M. A. Omari the global features are extracted at a low computational cost, and they have good noise resilience.

These features are less sensitive to noise and signature variations. So it does not give a high accuracy for skilled forgeries,

but it is suitable for random forgeries and is better to be combined with other types of features [8].

Samuel and Samuel proposed an off-line signature verification technique that used three new feature sets

extracted from a static image of signatures. The three feature sets were image cell size, image centre angle relative to the

cell lower right corner and pixels normalized angle relative to the lower right corner [9].

A. C. Ramachandr et al. proposed a Cross-validation for Graph Matching based offline Signature Verification

(CGMOSV) algorithm. The dissimilarity measure between two signatures in the database was determined by (i)

constructing a bipartite graph (ii) obtaining complete matching in and (iii) finding minimum Euclidean distance by

Hungarian method. Using Cross-validation principle reference signatures were selected and an optimum decision threshold

value was determined. The threshold value was used to compare and authenticate the test signature. They observed that

FRR, FAR and EER values were improved compared to the existing algorithm [10].

E. zgndz et al. From results obtained by the researchers in the field of offline signature verification, it is

noticed that the statistical approach, (HMMs, Bayesian etc.) can detect causal and skilled forgeries. Recognition rate of

95% was reported to be achieved using SVM [11].

PROPOSED METHODOLOGY

In this paper, statistical features have been used, as using statistical information

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