Offline handwritten signature verification using radial basis function neural network

Abstract

This study investigates the effectiveness of Radial Basis Function Neural Networks (RBFNNs) for Of- fline Handwritten Signature Verification (OHSV). A signature database is collected using intrapersonal variations for evaluation. Global, grid and texture features are used as feature sets. A number of exper- iments were carried out to compare the effectiveness of each separate set and their combination. The system is extensively tested with random signature forgeries and the high recognition rates obtained demonstrate the effectiveness of the architecture. The best results are obtained when global and grid features are combined producing a feature vector of 592 elements. In this case a Mean Error Rate (MER) of 2.04% with a False Rejection Rate (FRR) of 1.58% and a False Acceptance Rate (FAR) of 2.5% are achieved, which are generally better than those reported in the literature.