Omni-directional face detection based on real AdaBoost
Chang Huang
Bo Wu
Haizhou AI
Shihong Lao
Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China;
This paper appears in: Image Processing, 2004. ICIP '04. 2004 International Conference on
Publication Date: 24-27 Oct. 2004
Volume: 1,
On page(s): 593- 596 Vol. 1
ISSN: 1522-4880
ISBN: 0-7803-8554-3
INSPEC Accession Number: 8402632
Digital Object Identifier: 10.1109/ICIP.2004.1418824
Current Version Published: 2005-04-18
Abstract
We propose an omni-directional face detection method based on the confidence-rated AdaBoost algorithm, called real AdaBoost, proposed by R.E. Schapire and Y. Singer (see Machine Learning, vol.37, p.297-336, 1999). To use real AdaBoost, we configure the confidence-rated look-up-table (LUT) weak classifiers based on Haar-type features. A nesting-structured framework is developed to combine a series of boosted classifiers into an efficient object detector. For omni-directional face detection, our method has achieved a rather high performance and the processing speed can reach 217 ms per 320×240 image. Experiment results on the CMU+MIT frontal and the CMU profile face test sets are reported to show its effectiveness.
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