On the use of quality measures in face and speaker identity verification based on video and audio streams

This study addresses the advantage of adding quality information of the biometric signals into a multimedia-based (video and audio) identity verification system. The quality information of the biometric signals can be used in several ways and stages in the biometric system. In this study, the authors introduce quality-based decisions in two stages: score normalisation and frame selection. Quality-based score normalisation helps to handle quality dependent drifts in the scores distributions. We derive a necessary and sufficient condition for reducing error when introducing quality-based score normalisation and present a score normalising technique. Additionally, the number of frontal faces and speech vectors extracted from the video and audio streams allows quality-based selection of frames, both in training and test, to preserve quality in the statistical representation of the signals. For these two stages we defined some quality measures for speaker and frontal face signals and run experiments to show the reliability of the proposed techniques over the BANCA database.