Face Detection Study based on Skin Color and Improved Adaboost Algorithm

For color images in a complex background, we cannot be able to detect faces quickly. So we put forward an algorithm, which is based on skin color feature and the improved AdaBoost algorithm. First, through the skin color detection to excluding large amounts of complex background of non-face, after that define the face candidate regions. Besides, when the image is darkness, we will increase the light treatment, afterwards use AdaBoost algorithm to detect the human face, to improve the accurate rate of face detection system and reduce the error rate. In addition, based on the AdaBoost algorithm of former research, we add new Haar features and modify the weight of its update method, so under the condition of the less weak classifier, the AdaBoost algorithm's training speed much faster, and to prevent the excessive distribution in the process of the training. The experimental results show the proposed method has great improvement for face detection.