CONFIDENCE-AWARE CLUSTERED LANDMARK FILTERING FOR HYBRID 3D FACE TRACKING

The detection of facial landmarks in 2D images has received a great attention in the last decade, as it is a key step for several computer-vision-related applications. Most of the approaches are focused on still images, and are extended to videos by using a tracking-by-detection scheme. In this work, we propose a frame-to-frame tracking module based on grouped-landmark Kalman filters that can be integrated into existing deep-learning-based 3D face alignment pipelines. This method improves the landmark accuracy in cases with large occlusion, extreme head poses and blurriness that affect existing approaches. Our experiments on the Menpo 3DA-2D benchmark show improvements on model-free and 3D-model-based face alignment approaches.