Improvement And Implementation Of AdaBoost Face Detection Algorithm

Face detection is the first phase of face anlysis, the problem refer to face detection is to determinate whether there are human faces in the image, if so, then locate the human faces in the image. Face detection can be used in many fields, and it is one of the most important steps to implement machine intelligence.AdaBoost algorithm is a fast face detection algorithm presented in 2001, it is a mile-stone in the field of object detection. AdaBoost theory can transform weak learning to strong learning, theoretically, if there are enough samples, enough features, and the training is absolutely adequate, the error rate of the classifiers that generated by AdaBoost algorithm is unlimited near zero. But, as the number of samples increases and the number of features increases, the training time of AdaBoost algorithm becomes incredible long. Also, because the standard detection algorithm detects the object by searching the entire image one subwindow by one subwindow. When the image is too big, the detection time is also too long, so it can't be used in a real-time environment.This paper describes three improvements on the AdaBoost algorithm. Firstly, through the decomposition of the two dimensional feature matrix and the using of parallel computing in AdaBoost training algorithm, the training speed will be increased significantly in multi-machine training. Secondly, by the replacement of traditional serial detection with parallel detection, the detection speed will be increased remarkably when multi-processor machine is used for detection. Thirdly, by using a dynamic step strategy to replace the constant step strategy, the number of windows to be detected will be decreased observably, so the detection speed will be increased significantly.