Proposed optimization for AdaBoost-based face detection

In this paper, a novel approach is proposed for face detection in still image based on the AdaBoost algorithm. First, face candidates are detected by AdaBoost Algorithm. Since a lot of influence might exist, such as size of the image, illumination and noise, some non-faces windows might also be detected as face candidates, or some faces might be missed. In order to solve these problems and get better performances, we take use of skin color information in the YCbCr color space together with the edge information of the color image. In this way, we are able to remove some non-faces that have been wrongly detected as faces and add some possible missed faces as well. Experimental results show that the hit rate could be improved and false alarm could also be reduced by this method.