Error handling in multimodal biometric systems using reliability measures

In this paper, we present a framework for predicting and correcting classification decision errors based on modality reliability measures in a multimodal biometric system. In our experiments we use face and speech experts based on a recently proposed framework which uses Bayesian networks. The expert decisions and the accompanying information on their reliability are combined in a decision module that produces the final verification decision. The proposed system is consequently shown to yield higher decision accuracy than the corresponding unimodal systems.