A color and feature-based approach to human face detection

Most existing face detection approaches have assumptions, which make them applicable only under some specific conditions. In this research, we propose a combination of the color-based approach and the feature-based approach to detect faces in a color image. This combined approach will allow detecting faces in more general circumstances. As an initial step, the skin color will be utilized to separate the faces from other complex background. We develop a color-based face detector, which is used to find the face boundaries in the image. The color-based detector reliably isolates any skin-colored region found in the image. However, the regions identified may sometimes consist of other skin-colored objects as well. The facial features, e.g. eyes and mouth, will be used to verify the existence of a face within a skin. The features will be extracted from the skin region and grouped into face candidates. To reliably select the correct face candidate, belief networks are developed to represent the relationships among features and to reinforce their probabilities. Some results are included to demonstrate the capability of the combined approach in detecting faces with different poses, multiple faces, and occlusion.