Handwritten static signature verification performed using wavelet transforms and neural networks

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.

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