Development of Writer Independent Offline Signature Verification System through Multiple Classifier and Geometric Features

Abstract

The signature is a very significant trait of an individual which serves not only for the identification of an individual but also for establishing the genuineness of official documents. The aim of this work is to investigate the scope of geometric features to develop a proficient offline signature verification system through multiple classifiers using writer-independent approach. The key focus of this work is to minimize the false acceptance rate for the simulated forgery. The classification task is accomplished through Support vector machine with Gaussian radial basis function and polynomial kernel. The k-fold cross validation method is utilized to construct the multiple classifier system of diverse classifiers. The genuine and random forgery signatures are considered to train the classifiers of multiple classifier system whereas genuine, random forgery and simulated forgery signatures are utilized to perform the testing process. A public signature database named GPDS is utilized to assess the performance of the proposed system. The simulation study reveals FRR of 7.00%, FARR of 0.00%, FARS of 0.00% thereby AER of 2.33% as the best result.