Uncertainty based classification fusion - a soft-biometrics test case

We address the problem of classification of data with low separability. We adopt a Bayesian approach, with discriminant functions expressing a posteriori class probabilities. We propose a novel classification scheme incorporating classification error probability estimates in the decision process. We extend this approach into a classifier fusion framework. Presented methods are evaluated in the context of user authentication, using multimodal biometrics. Results on real data confirm the usefulness of the proposed method, outperforming the corresponding deterministic classifier.