Face Detection System Based On Complexion Model And Adaboost Arithmetic

Face Detection is a process that detecting the human faces from images or videos and distill the characters. It is a key point of Face recognition and Facial feature extract. At the same time, It has important applicable value in some places such like track, pass check and medical treatment.Face Detection is a complex problem because of it influenced by background, light and head's posture. Based on the study on Face Detection technology, Noise Filter, Detection Character and Precision Improve are the main research directions in this paper.Compensate the picture's light and then filter the interference color by using reference white arithmetic and average evaluation arithmetic for filter the noise. Median arithmetic is used to separate the background, Median difference arithmetic is used to separate moving information, filter the background and expedite the detection. Choose the complexion as detection character. bring forward a arithmetic that synthesize Stylebook Distill and Fisher judgment. Improve the complexion clustering in RGB and YC_bC_r color space and gained the complexion distributing. Account out the resemble image. Analyze the resemble image by fisher judgment and gained the binary image. Search the connect areas and get the complexion windows. Complexion Detection's not high precision, training a series sort implement include three strong sort implements to detect the complexion windows subtly. Each strong sort implement is buildup with lots of weak sort implements. Finally detect the eyes character by the complexion windows' landscape orientation integral. Detect the lips character by analyzing the R channels signal.By using complexion and Adaboost arithmetic can eliminate the noise signal effectively. It can both pledge the high detection rate and the low error judge rate and improve the system's operation speed. The research has some theory and application value.