Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication

Abstract

We present three new voting schemes for multiclassifier biometric authentication using a reliability model to influence the importance of each base classifier's vote. The reliability model is a meta-classifier computing the probability of a correct decision for the base classifiers. It uses two features which do not depend directly on the underlying physical signal properties, verification score and difference between user-specific and user-independent decision threshold. It is shown on two signature databases and two speaker databases that this reliability classification can systematically reduce the number of errors compared to the base classifier. Fusion experiments on the signature databases show that all three voting methods (rigged majority voting, weighted rigged majority voting, and selective rigged majority voting) perform significantly better than majority voting, and that given sufficient training data, they also perform significantly better than the best classifier in the ensemble.