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An Automatic Off-Line Signature Verification and Forgery Detection System

Vamsi Krishna Madasu, Brian C. Lovell

Source Title: Pattern Recognition Technologies and Applications: Recent Advances

ISBN13: 9781599048079|ISBN10: 1599048078|ISBN13 Softcover: 9781616926922|EISBN13: 9781599048093

DOI: 10.4018/978-1-59904-807-9.ch004

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MLA

Madasu, Vamsi Krishna, and Brian C. Lovell. "An Automatic Off-Line Signature Verification and Forgery Detection System." Pattern Recognition Technologies and Applications: Recent Advances, edited by Brijesh Verma and Michael Blumenstein, IGI Global, 2008, pp. 63-89. https://doi.org/10.4018/978-1-59904-807-9.ch004

APA

Madasu, V. K. & Lovell, B. C. (2008). An Automatic Off-Line Signature Verification and Forgery Detection System. In B. Verma & M. Blumenstein (Eds.), Pattern Recognition Technologies and Applications: Recent Advances (pp. 63-89). IGI Global. https://doi.org/10.4018/978-1-59904-807-9.ch004

Chicago

Madasu, Vamsi Krishna, and Brian C. Lovell. "An Automatic Off-Line Signature Verification and Forgery Detection System." In Pattern Recognition Technologies and Applications: Recent Advances, edited by Brijesh Verma and Michael Blumenstein, 63-89. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-807-9.ch004

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Abstract

This chapter presents an off-line signature verification and forgery detection system based on fuzzy modeling. The various handwritten signature characteristics and features are first studied and encapsulated to devise a robust verification system. The verification of genuine signatures and detection of forgeries is achieved via angle features extracted using a grid method. The derived features are fuzzified by an exponential membership function, which is modified to include two structural parameters. The structural parameters are devised to take account of possible variations due to handwriting styles and to reflect other factors affecting the scripting of a signature. The efficacy of the proposed system is tested on a large database of signatures comprising more than 1,200 signature images obtained from 40 volunteers.

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An Automatic Off-Line Signature Verification and Forgery Detection System

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