Study of fast Adaboost face detection algorithm

For the time-consuming problem of Adaboost face detection algorithm in the training classifier process, a detailed analysis of Adaboost algorithm is carried out, the four-point average method is proposed to speed up looking for the best weak classifier. Using this method, for each feature f, the corresponding feature value of all training samples are calculated and ordered from small to large, a average values of four adjacent feature are found, the average is looked as a threshold to calculate the error rate and find the best weak classifier. Using different partial occlusion face samples train classifier to achieve partial obscured face detection. The experimental results show that the method can significantly improve training speed, shorten training time, and accurately detect partially obscured faces.