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 s...

Full description

Saved in:
Bibliographic Details
Published inProceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) Vol. 1; pp. 580 - 585 Vol. 1
Main Authors Ben-Yacoub, S., Luttin, J., Jonsson, K., Matas, J., Kittler, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1999
Subjects
Biometrics
Error analysis
Face recognition
Kernel
Probes
Protocols
Robustness
Spatial databases
Speech
System testing
Online AccessGet full text

Cover

More Information
Summary: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.
ISBN:9780769501499
0769501494
ISSN:1063-6919
DOI:10.1109/CVPR.1999.786997