Robust head pose estimation by machine learning

Author

Wang, Ce ; Brandstein, Michael

Author_Institution

Div. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA, USA

Volume

3

fYear

2000

fDate

2000

Firstpage

210

Abstract

Support vector machines are applied for estimating the head orientation angle of talkers in a video environment. The procedure is capable of accurately evaluating head orientations over a complete 360 degree interval and has been designed to function as part of an existing real-time, multi-talker tracking system. By relying on a facial criterion that is easily extracted from video images acquired across a range of lighting and zooming conditions, the estimator is designed to be effective in practical situations such as those encountered in video conferencing or surveillance scenarios

Keywords

feature extraction; image classification; learning (artificial intelligence); learning automata; parameter estimation; surveillance; teleconferencing; video signal processing; facial criterion; head orientation angle estimation; lighting conditions; machine learning; real-time multi-talker tracking system; robust head pose estimation; subset classification; support vector machines; surveillance scenarios; video conferencing; video environment; video images; zooming conditions; Cameras; Eyes; Face detection; Facial features; Machine learning; Magnetic heads; Mouth; Robustness; Support vector machines; Videoconference;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2000. Proceedings. 2000 International Conference on

Conference_Location

Vancouver, BC

ISSN

1522-4880

Print_ISBN

0-7803-6297-7

Type

conf

DOI

10.1109/ICIP.2000.899332

Filename

899332