On-line handwritten signature verification using wavelets and back-propagation neural networks

Abstract

This paper investigates dynamic handwritten signature verification (HSV) using the wavelet transform with verification by the backpropagation neural network (NN). It is yet another avenue in the approach to HSV that is found to produce excellent results when compared with other methods of dynamic, or on-line, HSV. Using a database of dynamic signatures collected from 41 Chinese writers and 7 from Latin script we extract features (including pen pressure, x and y velocity, angle of pen movement and angular velocity) from the signature and apply the Daubechies-6 wavelet transform using coefficients as input to a NN which learns to verify signatures with a False Rejection Rate (FRR) of 0.0% and False Acceptance Rate (FAR) less of than 0.1.