A Knowledge-Based Approach for Fast Human Face Detection
JIANG Jun, ZHANG Gui lin (Institute for Pattern Recognition and Artificial Intelligence Key Laboratory of Education Ministry for Image Processing and Intelligent Control, Wuhan 430074)
For the speed of detecting human faces in single grayscale scene imgae is slowly, an approach of knowledge based fast face detection is proposed. In the method a tri part mosaic image model is supplied, which is consistent with physical structure features of human face. Basing on this model, knowledge base, including information of edge and gray in images, is established by analyzing fairly enough human face images. Moreover, in order to enhance the speed of detecting face, hierarchical detection processes is adopted. In the process of rough face detection, a new coarse detection method--extended projection which is according with the physical structure features of eyes is presented and applied, which makes it possible to get different scale face candidate region, thus reduces the computation time of face detection greatly. Our experimental results indicate that the approach is high robust, and it is fit to solve the problem of multi face detection in complex background. Because of the algorithm's simpleness, it can be easily achieved by hardware, and this will shorten the time consuming on face detection. This made it possible to detect faces in real time. So it has wide application perspective at fields of visual telephone, intelligent monitor etc.