Automatic online signature verification using HMMs with user-dependent structure

Abstract

A novel strategy for Automatic online Signature Verification based on hidden Markov models (HMM) with user-dependent structure is presented in this work. Under this approach, the number of states and Gaussians giving the optimal prediction results are independently selected for each user. With this simple strategy just three genuine signatures could be used for training, with an EER under 2.5% obtained for the basic set of raw signature parameters provided by the acquisition device. This results increment by a factor of six the accuracy obtained with the typical approach in which claim-independent structure is used for the HMMs.