Audio-Visual Person Verification

. In this paper we investigate benefits of classifier combination (fusion) for a multimodal system for personal identity verification. The system uses frontal face images and speech. We show that a sophisticated fusion strategy enables the system to outperform its facial and vocal modules when taken seperately. We show that both trained linear weighted schemes and fusion by Support Vector Machine classifier leads to a significant reduction of total error rates. The complete system is tested on data from a publicly available audio-visual database (XM2VTS, 295 subjects) according to a published protocol. 2 IDIAP--RR 98-18 1 Introduction Recognition systems based on biometric features (face, voice, iris, etc ...) have received a lot of attention in recent years Most of the proposed approaches focus on mono-modal identification. The system uses a single modality to find the closest person to the user in a database. Relatively high recognition rates were obtained for different modalities..