Real Time 3D Face Pose Tracking From an Uncalibrated Camera

We propose a new near-real time technique for 3D face pose tracking from a monocular image sequence obtained from a n uncalibrated camera. The basic idea behind our approach is that instead of treating 2D face detection and 3D face pose estimation separately, we perform simultaneous 2D face detection and 3D face pose tracking. Specifically, 3D face pose at a time instant is constrained by the face dynamics using Kalman Filtering and by the face appearance in the image. The use of Kalman Filtering limits possible 3D face poses to a small range while the best matching between the actual face image and the projected face image allows to pinpoint the exact 3D face pose. Face matching is formulatd as an optimization problem so that the exact face location and 3D face pose can be estimated efficiently. Another major feature of our approach lies in the use of active IR illumination, which allows to robustly detect eyes. The detected eyes can in turn constrain the face in the image and regularize the 3D face pose, therefore the tracking drift issue can be avoided and the processing can speedup. Finally, the face model is dynamically updated to account for variations in face appearances caused by face pose, face expression, illumination and the combination of them. Compared with the existing 3D face pose tracking techniques, our technique has the following benefits. First, our technique can track face and face pose simultaneously in real time, which as been implemented as a real time working system. Second, only one uncalibrated camera is needed for our technique, which will make our system very easy to set up. Third, our technique can handle facial expression change, face occlusion and illumination change, which will make our system work under real life conditions.