This paper presents a new method for 3D face pose tracking in color image and depth data acquired by RGB-D (i.e., color and depth) cameras (e.g., Microsoft Kinect, Canesta, etc.). The method is based on a particle filter formalism. Its main contribution lies in the combination of depth and image data to face the poor signal-tonoise ratio of low quality RGB-D cameras. Moreover, we consider a visibility constraint to handle partial occlusions of the face. We demonstrate the accuracy and the robustness of our method by performing a set of experiments on the Biwi Kinect head pose database.
Finally, REF , based on a particle filter formalism, presents a new method for 3D face pose tracking in color images and depth data acquired by RGB-D cameras.
3D Face Pose Tracking using Low Quality Depth Cameras
VISAPP
Computer Science