Audio-Visual Person Verification
Typ dokumentu
příspěvek z konference - tištěnýAutor
Ben-Yacoub, S.
Luttin, J.
Jonsson, K.
Matas, J.
Kittler, J.
Práva
© 1999 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Metadata
Zobrazit celý záznamAbstrakt
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.
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