Real-time Face Pose Estimation In Video Sequence

Face Pose Estimation is a process that is through dealing with the static images orvideo sequences to determine the posture of human face in three dimensional space. As animportant research topic in the field of CV(computer vision), Face Pose Estimation inhuman computer interaction, intelligent video surveillance, virtual reality, as well as facerecognition has a wide application prospect and great value, and it is the research hotspot inrecent years.At present, a majority of Face Pose Estimation methods is for static images, which iscomplex, of high computational complexity, and is not suitable for estimating dynamicvideo images. In this thesis, a novel Face Pose Estimation method is proposed, which isbased on estimating the relative location of mouth in whole face. The method is simple,effective, fast and fully able to meet the real time requirement for estimating face pose invideo sequences.Estimating face pose in video sequences relates to Face Detection, Face Tracking,Face Pose Estimation and other related topics. To begin with, a comparison and summary ofthe existing face detection methods is given, color model which is non sensitive for posturechange is chosen for obtaining the detected face region in first few frames in the videosequences, and then the detected face region is being tracked by the CAMShift(Continuously Adaptive Mean Shift) algorithm, and at the same time which is fitted byellipse model, which is in order to locate human face region in video sequences.Subsequently, using the information about lip color, mouth is located in the detected faceregion and according to the relative location of mouth in whole face and the direction angleof ellipse model for fitted face regions, estimating human face posture in video sequences ismade, and then a prototype system for estimating the face posture is implemented, andsome experiments are made using the recorded video files, which has verified the feasibilityof the method.