Handwritten static signature verification performed using wavelet transforms and neural networks
Daniel K. R. McCormack, John F. Pedersen · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
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.