Research and implementation of real-time face detection, tracking and protection

Privacy protection in video images is becoming one of the research focuses in the field of remote collaborative system. In this paper, a method for face detection, tracking and privacy protection is presented. According to skin-color distribution in the color space, we developed a statistical skin-color model through interactive sample training. Using this model we convert the color image to binary image and then segment face candidate region. Then we use a facial feature matching scheme for further detection. The presence or absence of a face in each region is verified by means of mouth detector. Real time detection and tracking can be achieved by using this method in video images. In order to speed up tracking, we improve the traditional method by adding motion prediction, which works better when several disturbing objects appear simultaneously. Finally we make the tracking region blurring and transmit the frames to the remote collaborative sites to obtain the privacy protection. The level of privacy protection can be dynamically adjusted according to collaborators´ requests and credibility of remote sites. The experiment results show the proposed method not only has high speed and efficiency, but also is robust to head rotation to some extent.