Study of the cost-sensitive AdaBoost face detection algorithm

It is the mainstream method that in human face detection and recognition with AdaBoost as the representative based on statistical learning method. Detection rates have reached a high level, and can achieve real-time detection. However, AdaBoost algorithm treats equally for different categories, there is no distinction between the cost of the different error categories. This paper presents a new cost-sensitive AdaBoost-based face detection algorithm, ensuring the detection rate and speed, effectively reducing the false detection rate, and improving the detection accuracy.