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The Scientific World Journal
Volume 2014 (2014), Article ID 381469, 8 pages
Research Article

Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis

1Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
2Department of Systems and Networking, Universiti Tenaga Nasional, Jalan IKRAM-Uniten, 43000 Kajang, Malaysia

Received 28 January 2014; Accepted 12 June 2014; Published 14 July 2014

Academic Editor: Jian Li

Copyright © 2014 Vahab Iranmanesh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.