Reliability-based decision fusion in multimodal biometric verification systems

We present a methodology of reliability estimation in the multimodal biometric verification scenario. Reliability estimation has shown to be an efficient and accurate way of predicting and correcting erroneous classification decisions in both unimodal (speech, face, online signature) and multimodal (speech and face) systems. While the initial research results indicate the high potential of the proposed methodology, the performance of the reliability estimation in a multimodal setting has not been sufficiently studied or evaluated. In this paper, we demonstrate the advantages of using the unimodal reliability information in order to perform an efficient biometric fusion of two modalities.We further show the presented method to be superior to state-of-the-art multimodal decision-level fusion schemes. The experimental evaluation presented in this paper is based on the popular benchmarking bimodal BANCA database.