Wide-range, person- and illumination-insensitive head orientation estimation

Ying Wu, Kentaro Toyama

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

36 Scopus citations

Abstract

We present an algorithm for estimation of head orientation, given cropped images of a subject's head from any viewpoint. Our algorithm handles dramatic changes in illumination, applies to many people without per-user initialization, and covers a wider range (e.g., side and back) of head orientations than previous algorithms. The algorithm builds an ellipsoidal model of the head, where points on the model maintain probabilistic information about surface edge density. To collect data for each point on the model, edge-density features are extracted from hand-annotated training images and projected into the model. Each model point learns a probability density function from the training observations. During pose estimation, features are extracted from input images; then, the maximum a posteriori pose is sought, given the current observation.

Original languageEnglish (US)
Title of host publicationProceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000
PublisherIEEE Computer Society
Pages183-188
Number of pages6
ISBN (Print)0769505805, 9780769505800
DOIs
StatePublished - Jan 1 2000
Event4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000 - Grenoble, France
Duration: Mar 28 2000Mar 30 2000

Publication series

NameProceedings - 4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000

Other

Other4th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2000
CountryFrance
CityGrenoble
Period3/28/003/30/00

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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