A Method Of Face Detection Based On Improved YUV Colorspace

Face detection is an image processing course, ascertaining face's location, size and amount in an image. And face detection is the precondition of face recognition, human-computer interface and intelligent scene supervision system. Research on face detection will be paid more attention, with computer application being popular and its capabilities improved and researches growing up in image processing and mode recognition field. There are a lot of arithmetics of face detection in the past decades. But the rate, precision and robustness of arthmetics are not ideal. The research works in this paper is as following.The key problem of face detecting re is extracting features in gray space.. In this paper, we proposed a method of Feature Model Based Face Detecting. It extracts features from eyes and nose, and then constructs a triangular feature mold component with eyes and nose. The search is taken in the candidate feature image by changing the size of blocks step by step. This processing can obtain separate points of feature mold. And finally proposed a strategy based on the knowledge about facial structure/distribution to search the feature mold. Experimental results prove that this method can rapidly and accurately detect face in an image with complex background. Furthermore, it can perform well for multiple faces.Considering the proportion of different chroma of skin color, we proposed a method of skin detection based on improved YCbCr color space. Instead Cb of Cg. Experimental results prove that skin pixel have better clustering in improved YCbCr color space.Under simple skin underground, change axis on YUV color space, in order to increasing distribution deference between skin sample and lip sample, lastly, using the geometry feature of lip on human face to locate face. Experimental results prove that the method can provide lower false rate,and achieve good detection performance in multi-face image. Lower complexity suits to reality.