Research On Face Detection Based On Skin Color Segmentation And Real-adaboost Algorithm

Face detection is a smart technology which use computer to detect the faceposition、posture and size in the input pictures, it have been widely used in biometricauthentication, automatic monitoring, and human-computer interaction. Face detectionis fraught with difficulties, because human face is non-rigid object in a complexenvironment. Domestic and foreign scholars have invested a great deal of researching,in order to find a universal detection algorithm, but effective little. The existingdetection algorithms have their own limitations. To solve this problem, this paperinducted and summary of existing main detection algorithm. Then put forward a rapidface detection system combined skin color detection and Real-Adaboost algorithm, themain content:1、Research how a bad light affects the color information, design the skin colorsegmentation scheme base on YCbCr and Gaussian model. In order to obtain the binaryimage, using an adaptive threshold separation complexion likelihood diagram,morphological filtering and exclude non-face structure to get candidate face region.2、Research face detection method Based on Adaboost,introduce how to useintegral image to calculation Haar features. Deeply study their training methods andcomposition of the principle of the weak and strong classifier. Given the detailed designapproach of the face detector which base on Adaboost algorithm3、Supplement and improve the face detection method which use the classicalAdaboost algorithm, expanded the types of Haar features, introduced Haar templatewith a rotation of45°. For Adaboost output is a discrete value problem, useReal-Adaboost instead of it. For the equidistant sample space divided way not wellportrays the positive and negative sample boundary problem, proposed an adaptivesample space divided way. In order to change the Real-Adaboost non-monotonicityproblem, introduce a floating search strategy. Finally use the universal face database aspositive samples, use bootstrap method collected negative samples, employ theimproved Real-Adaboost train a cascaded face detector.4、Analyzed the advantages and disadvantages of skin-based and Adaboost-based face detection method. Take advantage of their complementary nature, design a facedetection system combined skin color detection and Real-Adaboost algorithm, in theearly detection, skin color quickly ruled out a large number of non-face area in thepicture, using cascaded face detector to secondary positioning, by experimental analysisshows that this method contribute to improve the detection speed and reduce the falsedetection rate, has positive significance to enhance the performance of the facedetection system.