Signature verification using radial basis function classifier

Abstract

Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. The Verification of handwritten Signature, which is a behavioral biometric, can be classified into off-line and online signature verification methods. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: online Signature Verification This paper addresses using ensemble approach of Radial Basis Function Classifier for online Signature Verification. Online signature verification, in general, gives a higher verification rate than off-line verification methods, because of its use of both static and dynamic features of problem space in contrast to off-line which uses only the static features. We show that proposed ensemble of Radial Basis Function classifier is superior to individual approach for Signature Verification in terms of classification rate.