Fast Face Detection Based On Knowledge Method And Self-adoptive Template Matching

Face detection is to determine the size and location of a human face in an image which is being detected. Face detection is the necessary earlier task in face recognition process, aiming at separating the face area from the background image. Face recognition system adapted to some general environmental image to some extent needs a Robust,high efficient and real-time face detection system. In recent years, with the technological development of computer vision and pattern recognition, as well as the improving of hardware speed and cost reducing, the identity authentication technology based on biometrics has developed rapidly .Compared with other biometrics technology, face detection is more direct,much friendlier,and more convenient and it is one of the hot research fields, drawing more and more attention.This paper sets completing earlier preparation for face recognition as its objection, using skin color clustering, and it has designed and realized a human face detection algorithm under a complex background. This paper includes four aspects: image capture,image preprocessing,segmentation of face skin color and face detection. First, image capture and pan-tilt control are firstly achieved based on HiKivision's image capture board. And then it comes to preprocessing the real-time images, during which median filter, lighting compensation and image gray processing are to be realized successively, what is more, a threshold value is presented to improve compensation effect on the base of light compensation algorithm of Rein-Lien Hsu etc. During the phase of skin segmentation after pretreatment, a non-linearity transform method of skin color segmentation is proposed, combining skin color clustering and edge detection. It first makes use of the skin color clustering characteristic of non-linearity transform to segment different skin color regions. Then it needs to decide whether local non-linearity information should be applied to go on segmenting the regions depending on their areas. Finally it detects and validates the candidate face region that has been segmented by human face detection algorithm. This paper puts forward a face detection algorithm which combines the detection method based on knowledge and self-adaptive template matching methods. As for the face region where the features are not obvious or some of which is covered, half-face template should be used to avoid missing detection of face region.