The Face Detection System Based On Adaboost Algorithm Design And Implementation,

Face detection is a research subject stemming from the request of face recognition, with the development of computer technology face detection is researched independently. Not only is face detection the basis of face recognition and facial expression recognition, having influence on the research of these subjects, but also have applied widely in the area of public security and safe guard etc.. Face detection meet great challenge because of changeable pattern of face, illumination of light and background scene etc..Face detection is a method to find whether a picture has face or not using image process, artificial intelligence and pattern recognition technology, to count the number and find the position of the detected faces if there is face in the picture. After comparing many methods the face detection system is constructed based on the classifier consisting of Haar-Like feature, AdaBoost algorithm and cascade theory.The classifier is trained based on AdaBoost algorithm, the face detection system consists of 3 independent subsystems: samples preprocess subsystem, classifier training subsystem, faces detection subsystem. The following methods are adopted to improve robustness and speed of this system: the pictures is preprocessed by image process technology, rotation, zooming, light compensation etc.; the rapid computing method of Integral Image are used; increasing the number of face pictures and add as many different skin color, different background scene, different illumination light pictures as possible to improve the accuracy of face detection; the method of zooming out the windows to detect ease the computing task compared with the conventional method of pyramid. These methods can improve the detection accuracy and decrease the detection time greatly, this system can meet the requirement of real-time detection.