OMNI-DIRECTIONAL FACE DETECTION BASED ON REAL ADABOOST (MP-L1)

Author(s) : Chang Huang (Department of Computer Science and Technology, Tsinghua University, China) Bo Wu (Department of Computer Science and Technology, Tsinghua University, China) Haizhou Ai (Department of Computer Science and Technology, Tsinghua University, Beijing 100084, P.R.China, China) Shihong Lao (Sensing Technology Laboratory, Omron Corporation, Japan)

Abstract : In this paper, we propose an omni-directional face detection method based on the confidence-rated Adaboost algorithm, called real Adaboost, proposed by Schapire and Singer. To use real Adaboost, we configure the confidence-rated look-up-table (LUT) weak classifiers based on the 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 7FPS on 320 by 240 images .Experiment results on the CMU+MIT frontal and the CMU profile face test sets are reported to show its effectiveness.

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