Handwritten static signature verification performed using wavelet transforms and neural networks

Daniel K. R. McCormack; John F. Pedersen

doi:10.1117/12.304906

26 March 1998 Handwritten static signature verification performed using wavelet transforms and neural networks

Daniel K. R. McCormack, John F. Pedersen

Proceedings Volume 3391, Wavelet Applications V; (1998) https://doi.org/10.1117/12.304906
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

Daniel K. R. McCormack and John F. Pedersen "Handwritten static signature verification performed using wavelet transforms and neural networks", Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); https://doi.org/10.1117/12.304906

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