Quality-Based Score Normalization for Audiovisual Person Authentication

This paper addresses the problem of biometric audiovisual person authentication in realistic acquisition conditions. Differences in environmental factors or acquisition devices between enrollment and test conditions modify the verification scores distribution and degrade verification performance if not taken into account. A theoretical framework that incorporates quality measures to biometric authentication is introduced. As a result, the necessary and sufficient condition that a quality measure must hold to enable improved verification performance is given. Two quality-based score normalization techniques are derived that successfully incorporate quality factors in the verification decision process. Experiments on the multimodal BANCA database for video-based face verification and speaker verification show a statistically significant verification performance improvement when using the proposed quality-based score normalization techniques.