Face Detection Based on Statistical Color Model and Haar Classifier

Abstract

The paper realizes the face detection algorithm based on the combination of the skin model and the Haar algorithm. Firstly, a platform for sample labeling was constructed, which combines the contour extraction algorithm with manual labeling. By labeling more than 10000 images obtained randomly from the Internet, a large training dataset is available. Then, a skin histogram, a non-skin histogram and a statistical skin model are constructed by analyzing the distribution of the skin and the non-skin color on the basis of a large training dataset. Based on this statistical color model, the skin area is detected and split from video files frame by frame. With the Haar Object Detection algorithm and the morphology algorithm such as erosion and dilation, the background noise and non-face areas are removed from the detected skin area and facial area is detected, which provides the basis for face recognition and the video-based visual speech synthesis. Compared with the Haar-based face detection method, our algorithm greatly improves the rate of correct detection and reduces the rate of the false positives.