Multi-Modal Person Authentication

Abstract

This paper deals with the elements of a multi-modal person authentication systems. Test procedures for evaluating machine experts as well as machine supervisors based on leave-one-out principle are described. Two independent machine experts on person authentication are presented along with their individual performances. These experts consisted of a face (Gabor features) and a speaker (LPC features) authentication algorithm trained on the M2VTS multi-media database. The expert opinions are combined yielding far better performances by using a trained supervisor based on Bayesian statistics than individual modalities aggregated by averaging