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Proceedings Paper

Handwritten static signature verification performed using wavelet transforms and neural networks
Author(s): Daniel K. R. McCormack; John F. Pedersen
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Paper Abstract

This paper investigates the use of various wavelet transform as a method of performing data reduction on static signature images presented to be backpropagation neural network. It is shown that a particular subset of 64 Daubechies D4 wavelet transform coefficients act as an efficient representation of a static signature image when sued to train a backpropagation network to perform static signature verification. Results indicate a signature verification performance of at least 95 percent.

Paper Details

Date Published: 26 March 1998
PDF: 10 pages
Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304906
Show Author Affiliations
Daniel K. R. McCormack, Cardiff Univ. of Wales (United Kingdom)
John F. Pedersen, Univ. of South Florida (United States)


Published in SPIE Proceedings Vol. 3391:
Wavelet Applications V
Harold H. Szu, Editor(s)

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