An Ensemble Based Offline Handwritten Signature Verification System

Alpana Deka,Lipi B Mahanta

doi:10.19139/soic-2310-5070-447

Abstract

In the field of security and forgery prevention, handwritten signatures are the most widely recognized biometric since long and also most practical. Although handwritten signature verification systems are studied using both On-line and Off-line approaches, Off-line signature verification systems are more difficult to compare to On-line verification systems. This is due to the lack of dynamic information, viz. a database which constantly stores the latest signature of the person. In the paper an approach using ensemble methods are adopted to classify a signature as forgery or not. In proposed system, three classifiers, viz, one unsupervised, viz. Fuzzy C-Means (FCM) and two supervised classifiers, viz. Naive Bayes (NB) and Support Vector Machine (SVM) are used as base classifiers. An attempt is made to compare the different approaches. We attempt both the categories of classification not only because both of them are applicable in this particular case but also with an objective of finding out which performs better. In most cases it is observed that Naive Bayes and Ensemble are comparable as they exhibit better performance than the other two. But among them, in most of the cases Ensemble classifier performs better than the Naive Bayes and consequently we have taken the Ensemble as a final classifier.

Highlights

A signature is nothing but a combination of letters, words and special symbols
We have developed our algorithm for verification of signatures with Fuzzy C-Means (FCM) technique on the assumption that if the cluster size is more than 2, the process will continue
Experiments were conducted with the methodology described above with various test signatures belonging to the categories genuine and skilled, simple and random type of forgery cases

Summary

Introduction

A signature is nothing but a combination of letters, words and special symbols. Depending on the nature of the samples acquisition method, a signature verification system may of online and offline [2, 3]. In a pattern recognition system, an ensemble classifier is the fusion of multiple base classifiers to predict a class label of an unseen instance [4]. The base classifiers can be aggregated with the help of combiners like simple majority voting, weighted majority voting, Bayesian combination, probabilistic approximation etc. The simple majority voting is found to be immensely used combiner due to its simplicity [5]. The output which appears most frequently as a vote for the base classifiers is considered as a required pattern of an instance [6]

Objectives

Methods

Results