Media-integrated biometric person recognition based on the Dempster-Shafer theory

Yoshiaki Sugie, Tetsunori Kobayashi

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The present paper describes a new integration method of speech and facial image information for person recognition problems based on the Dempster-Shafer probability theory. The Dempster-Shafer theory provides an attractive methodology by which to integrate multiple numerical evidences containing ambiguities. However, no concrete and reasonable methodology exists to enumerate the reliability of evidences. In the present paper, this problem is solved using the cumulative density function of both the correct and incorrect categories. The proposed enumerating method allows the Dempster-Shafer theory to be applied to media integration. We show that the total performance of person recognition, including rejection of unregistered users, is improved significantly using the proposed method.

Original languageEnglish
Pages (from-to)381-384
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume16
Issue number4
Publication statusPublished - 2002 Dec 1

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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