Con dence Measures for Multimodal Identity Veri cation ?
Multimodal fusion for identity veri cation has already shown great improvement compared to unimodal algorithms. In this paper, we propose to integrate con dence measures during the fusion process. We present a comparison of three di erent methods to generate such con dence information from unimodal identity veri cation systems. These methods can be used either to enhance the performance of a multimodal fusion algorithm or to obtain a con dence level on the decisions taken by the system. All the algorithms are compared on the same benchmark database, namely XM2VTS, containing both speech and face information. Results show that some con dence measures did improve statistically signi cantly the performance, while other measures produced reliable con dence levels over the fusion decisions.