Gabor attributes tracking for face verification

Baoxin Li, R. Chellappa

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

5 Scopus citations

Abstract

A method based on sequential importance sampling is proposed for tracking facial features on a grid with Gabor attributes. The motion of facial feature points is modeled as a global 2-D affine transformation (accounting for head motion) plus a local deformation (accounting for residual motion due to inaccuracies in 2-D affine modeling and other factors such as facial expression). Motion of both types is estimated simultaneously by the tracker: global motion is tracked by importance sampling, and residual motion is handled by incorporating local deformation into the measurement likelihood in computing the weight of a sample. While it has other applications in facial analysis, the method is particularly applicable to face verification because of a novel parametrization.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Pages45-48
Number of pages4
Volume1
StatePublished - 2000
Externally publishedYes
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period9/10/009/13/00

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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  • Cite this

    Li, B., & Chellappa, R. (2000). Gabor attributes tracking for face verification. In IEEE International Conference on Image Processing (Vol. 1, pp. 45-48)