Video Face Detection Based On AdaBoost

Face detection is the premise and foundation of face recognition,facial expressions extraction,it has been widely applied in video monitoring,and could monitor the targets without knowing or cooperating,and this process provide a powerful guarantee for intelligent video monitoring.This paper mainly studies the video face detection based on AdaBoost algorithm,due to video face detection has a lot of factors need to be considered,such as complex environments,multi faces,face rotation angles,and so on.However the existing detection algorithms fail to fully adapt to all situations.Thus,it is a challenging task to build a strong robust,high detection rate and low false detection rate face algorithm.Face detection based on AdaBoost algorithm has a good detection effect,and basically reach the effect of real-time face detection,so this paper adopts the algorithm for video detection,and the main work is done as follows:At first,in view of the problem that the excessive number of Haar features,which will lead to the time-consuming,during face detection classifier training process based on AdaBoost algorithm,An AdaBoost face detection training algorithm based on the "Big T" regions was proposed.The key areas of facial features extracted from 500 face samples to produce a 20*20 template,and the "Big T" type feature selection region union from the overlap regions,and Haar features can be optimized using the template which can be used to focus on the Haar features of the face.The experimental results shows that face detection rate in LFW,PKU database approximately equal to the detection rate of original Haar features,and undiminished the detection rate of original AdaBoost algorithm,the miss rate of multi face detection algorithm in PKU database is improved,and the training time of the AdaBoost algorithm is optimized.Secondly,in view of the video images collected by the monitoring equipment which inevitable has multiple faces,complex background,illumination interference,multi pose faces,individual using AdaBoost algorithm has a high error detection rate and miss rate,this paper propose a video face detection algorithm based on AdaBoost algorithm and YCgCr mixed Gaussian color model.By modeling the skin color samples in YCgCr color space,found that the luminance component also has approximate Gauss distribution characteristics,so the mixed Gauss skin model using the linear weighted method to construct Y and CgCr components,and using the mixed Gauss model to segment the skin color in the video image.Experimental results show that AdaBoost algorithm and the new skin color model for video face detection could avoid the influence of the complex backgrounds,and could achieve the purpose of reduce the false detection rate and miss rate.