Audio-Visual Person Verification

In this paper we investigate benefits of classifier combination 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 according to a published protocol.


Published in:
Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1999, Fort Collins, USA
Presented at:
Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1999, Fort Collins, USA
Year:
1999
Keywords:
Note:
IDIAP-RR 98-18
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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