STATISTICAL METHODS FOR ON-LINE SIGNATURE VERIFICATION

Abstract

Three methods for on-line signature verification are discussed in this paper. They are based on statistical models of features that summarize different aspects of signature shape and the dynamics of signature production. Two of the methods are based on the feature statistics of genuine signatures only. Of these two methods, the simpler one using a Euclidean distance error metric was found to have superior performance when tested on a database of 919 genuine signatures and 330 forgeries. Using a procedure for selecting the individual best 10 out of 22 features, the Euclidean distance method correctly classified 99.5% of the genuine signatures, while rejecting 86% of the forgeries. The third method uses statistical properties of the forgeries as well as the genuine signatures to develop a quadratic discriminant rule for classifying signatures. On the basis of the database used in this study, this method was generally better than the simpler Euclidean distance method. At the same 99.5% acceptance of the genuine signatures, the method rejected 90% of the forgeries.