Abstract: To accelerate the speed of face detection and get low false alarm rate, an approach of knowledge-based face detection is proposed. It integrates the skin-color model and the gravity-center template. In the process of rough detection, the skin-color model is used to segment the face like regions from any input image. The face-like regions are further checked out by matching the gravity-center template. As the physical structure of human face being taken into careful consideration, a dynamic three subsections distribution model of face, is proposed and used to establish a face knowledge base by analyzing large numbers of face images under different conditions. All the face-like regions are verified if they are genuine human faces based on the knowledge base of faces. The experimental results show that this approach is robust for human face images under complex background, different sizes and certain degree of rotation.