Technology And Implementation Of Face Detection In Video Based On Adaboosting Algorithm

A variety of detection algorithms have been proposed with the study of face detection. The factors of affecting the face detection is gradually overcome by the experts and scholars. The accuracy of face detection are constantly being refreshed and the time of face detection is been constantly reduced. But there is no good solution about the face detection in the video so far. This paper designs a video face detection prototype system, which can complete face detection in the static images and video and display face that has been marked in the detection.In this paper, Face detection system mainly includes the following four parts: 1) image preprocessing and segmentation of skin color; 2) the face region pre judgment; 3) expand the training samples of face detection algorithm; 4) determine the location of the frame which need to be detected based on frame rate of the video and the time of current image detection dynamically.In order to complete the skin color segmentation, I convert RGB color space to YCbCr color space in this paper, and then determine the rectangle region of the human face and mark the region. Using Adaboosting algorithm.to detect the region. The algorithm of face detection which has been trained can complete face detection on some images which include the part of human face because of adding a certain amount of local facial image in the training samples. Thus, the face detection accuracy was more than 80% in the average case. The detection time is reduced greatly after pre-processing. In the image which was in the range of 500300? pixel, the average time of detection is about to 60 ms in the hardware configuration of the current personal computer. Combining with dynamic choosing method of the video frame, the system can basically meet the real-time requirements of the video.I designed the prototype system using C++ programming language and OpenCV and then used a lot of different pictures video to test the system. Experimental result shows that the system has a good robustness. The result of face detection had a good performance. In some pictures, the system can detect the human face according to the part of the face of man. The system has a high reference value for real-time face detection in the video, and a certain application prospect.