Introduction of quality measures in audio-visual identity verification

Audiovisual identity verification exploits both image and audio information to improve the performance of the identification system. Unfortunately, both image and audio systems are sensitive to signal quality. In this paper, we propose a method to combine output classifiers based on both image and audio quality measures. We define classes of signal degradation within which we estimate the fusion weights and normalization parameters. Results of experiments on the BANCA database show that fusion using quality measures improves verification performance by 25% compared to the baseline fusion method.