Dynamic signature verification method based on association of features with similarity measures

Abstract

The paper presents a persons׳ verification method based on the dynamic features of a given signature. In the proposed approach features space is connected with the set of similarity measures which can be used in the signature verification process. Features and linked similarity coefficients create a new composed signature features. Composed features are then reduced in the Hotelling reduction process, where the most appropriate features together with the most distinctive similarity measures are determined for each person. Finally, it leads to reduction of a composed signature features space. Compared to other approaches proposed solution automatically selects the best discriminatory features and similarity measures.The obtained verification results confirm that the proposed method, compared to other verification techniques, is very efficient. Proposed approach has been checked in different experiments, where various classifiers have been taken into consideration – perceptron and Bayesian network based classifiers as well as k-NN, random trees, random forests and others.In this paper we have included the simulation results for the two available datasets of dynamic signatures: SVC2004 and MCYT databases.