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Online handwritten signature recognition by length normalization using up-sampling and down-sampling


Citation

Malallah, Fahad Layth and Syed Ahmad, Sharifah Mumtazah and Wan Adnan, Wan Azizun and Arigbabu, Olasimbo Ayodeji and Iranmanesh, Vahab and Yussof, Salman (2015) Online handwritten signature recognition by length normalization using up-sampling and down-sampling. International Journal of Cyber-Security and Digital Forensics, 4 (1). pp. 302-313. ISSN 2305-0012

Abstract / Synopsis

With the rapid advancement of capture devices like tablet or smart phone, there is a huge potential for online signature applications that are expected to occupy a large field of researches in forthcoming years. Online handwritten signature encounters difficulty in the verification process because an individual rarely produce exactly the same signature whenever he signs. This difference in the produced signature is referred to as intra-user variability. Verification difficulty occurs especially in the case where the feature extraction and classification algorithms are designed to classify a stable length vector of input features. In this paper, we introduce an efficient algorithm for online signature length normalization by using Up-Sampling and Down-Sampling techniques. Furthermore, online signature verification system is also proposed by using both Principal Component Analysis (PCA) for feature extraction and Artificial Neural Network (ANN) for classification. The SIGMA database, which has more than 6,000 genuine and 2,000 forged signature samples taken from 200 individuals, is used to evaluate the effectiveness of the proposed technique. Based on the tests performed, the proposed technique managed to achieve False Accept Rate (FAR) of 5.5% and False Reject Rate (FRR) of 8.75%.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
Publisher: The Society of Digital Information and Wireless Communications
Keywords: Artificial neural network; Authentication; Biometrics; Principal components analysis; Length normalization; Signature verification
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 21 Dec 2015 13:59
Last Modified: 21 Dec 2015 13:59
URI: http://psasir.upm.edu.my/id/eprint/34745
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