Improved face detection method based on AdaBoost algorithm

Focusing on the disadvantages of classical AdaBoost algorithm,this paper mainly analysed the issues of overfitting and distortion of sample weights in training process and come up with a new method to avoid the phenomenon of overfitting.The proposed approach set a weight threshold for each loop,and updated weight of sample according to whether the current weight was greater than the threshold,so that weights of hard samples would not expand too large.A cascade face detector was established using the method.The experimental results show that the new method will not lead to overfitting like classical AdaBoost often does,and it will reduce false alarm rate while holding a high detection rate.