Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion
This work addresses the need for integrated facial analysis in computer vision applications, though it appears incremental as it builds on existing cascade and model-based methods.
The paper tackled the problem of independent and sequential processing in facial behavior analysis by proposing a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, achieving robustness to facial occlusion with demonstrated effectiveness on benchmark databases.
Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. The existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, and the proposed model is robust to facial occlusion. Following a cascade procedure augmented with model-based head pose estimation, we iteratively update the facial landmark locations, facial occlusion, head pose and facial de- formation until convergence. The experimental results on benchmark databases demonstrate the effectiveness of the proposed method for simultaneous facial landmark detection, head pose and facial deformation estimation, even if the images are under facial occlusion.