Persian signature verification using improved Dynamic Time Warping-based segmentation and Multivariate Autoregressive modeling

In this paper, we present an online signature verification system based on dynamic time warping (DTW)-based segmentation technique combined with multivariate autoregressive (MVAR) modeling. We also use multilayer perceptron neural network architecture as data classifier. The input data that has been used is (x_j,y_j) coordinates of signatures drawn from a Persian database. We compare two different DTW algorithms in terms of their effect in improving the alignment between the signature sample and a master signature reference for the subject writer. Our database includes 1250 genuine signatures and 750 forgery signatures that were collected from a population of 50 human subjects. We used 75% of samples for training and 25% for testing. We achieved an accuracy of 88.8% for a skilled forgery test which is a very promising result.