A NEURAL-NETWORK APPROACH TO OFF-LINE SIGNATURE VERIFICATION USING DIRECTIONAL PDF

A neural network approach is proposed to build the first stage of an A utomatic Handwritten Signature Verification System. The directional Pr obability Density Function was used as a global shape factor and its d iscriminating power was enhanced by reducing its cardinality via filte ring. Various experimental protocols were used to implement the backpr opagation network (BPN) classifier. A comparison, on the same database and with the same decision rule, shows that the BPN classifier is cle arly better than the threshold classifier and compares favourably with the k-Nearest-Neighbour classifier.