3D Face Pose Tracking From an Uncalibrated Monocular Camera
Zhiwei Zhu, Qiang Ji
biometric, eye tracking, 3D, face expression, Kalman Filtering, pose tracking
We propose a new near-real time technique for 3D face pose tracking from a monocular image sequence obtained from an 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 formulated 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.