Title: Off-line Signature Verification (SV) using the Chi-square statistics

Authors: M. Taylan Das, L. Canan Dulger, H. Ergin Dulger

Addresses: Roketsan Company, 06780 Elmadag Ankara, Turkey. ' Faculty of Engineering, Mechanical Engineering Department, 27310 Gaziantep, Turkey. ' Faculty of Medicine, Gaziantep University, Forensic Science, 27310 Gaziantep, Turkey

Abstract: Off-line Signature Verification (SV) is performed using Particle Swarm Optimisation–Neural Network (PSO–NN) algorithm. The technique is based on NN approach trained with PSO algorithm. The presented verification system includes image-processing techniques and other mathematical tools in its structure. To test the performance of the proposed algorithm, three types of forgeries, namely random, unskilled and skilled, are examined. A database with 1350 skilled and genuine signatures taken from 25 volunteers is used for testing the algorithm. The experimental results are presented with comparisons on verification accuracy and statistical figures.

Keywords: signature verification; off-line verification; PSO; particle swarm optimisation; neural networks; Chi-square statistics; image processing; signature forgeries; security; biometrics.

DOI: 10.1504/IJBM.2011.037711

International Journal of Biometrics, 2011 Vol.3 No.1, pp.2 - 12

Published online: 25 Dec 2010 *

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