An improved AdaBoost face detection algorithm based on optimizing skin color model

This paper proposes a face detection algorithm combined skin color detection and improved AdaBoost algorithm. First, skin regions are segmented from the detected image, and candidate face regions are obtained in terms of the statistical characteristics of human face; Then focusing on the phenomena of overfitting in training process of classical AdaBoost algorithm, this paper proposes a novel method to update weight. At the same time, the process of constructing cascade classifier is added to training process. Finally, the candidate face regions are scanned by cascade classifier for more exact face orientation. A mass of experimental results show that the new approach obtains better results and improves detection performance obviously.