Omni-directional face detection based on real AdaBoost

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/spl times/240 image. Experiment results on the CMU+MIT frontal and the CMU profile face test sets are reported to show its effectiveness.