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26 March 1998 Handwritten static signature verification performed using wavelet transforms and neural networks
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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