关键词: 人脸检测, AdaBoost算法, 运动特征, 光流, 主成分分析

Abstract: With the main trend of high-revolution remoter in video capture, the fundamental issue of AdaBoost algorithm is the amount of detection windows placed in the cascade classifiers. The hierarchical duplex AdaBoost algorithm is proposed to accelerate the detection speed. Based on the global target motion and local face feature, it extracts the target motion ranges from the relativity based on the patch, gets the sub-space of feature combining with Principal Component Analysis（PCA） based on the motion features， obtains the set of candidate windows by the sub-space projection. Compared with experiment results, on the video frame of 640×480 and 1?280×720, the hierarchical duplex AdaBoost algorithm has higher sub-window placement rate and stable detection speed, and the average detection speed is 28 f/s and 6 f/s respectively. So it is adapted to the real-time detection.

Key words: face detection, AdaBoost algorithm, moving feature, optical flow, Principal Component Analysis（PCA）