Multi-modal person identification in a smart environment

Hazim Kemal Ekenel, Mika Fischer, Qin Jin, Rainer Stiefelhagen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we present a detailed analysis of multi-modal fusion for person identification in a smart environment. The multi-modal system consists of a video-based face recognition system and a speaker identification system. We investigated different score normalization, modality weighting and modality combination schemes during the fusion of the individual modalities. We introduced two new modality weighting schemes, namely, the cumulative ratio of correct matches (CRCM) and distance-to-second-closest (DT2ND) measures. In addition, we also assessed the effects of the well-known score normalization and classifier combination methods on the identification performance. Experimental results obtained on the CLEAR 2007 evaluation corpus, which contains audio-visual recordings from different smart rooms, show that CRCM-based modality weighting improves the correct identification rates significantly.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Country/TerritoryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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