  There have been tremendous improvements for facial landmark detection on
general "in-the-wild" images. However, it is still challenging to detect the
facial landmarks on images with severe occlusion and images with large head
poses (e.g. profile face). In fact, the existing algorithms usually can only
handle one of them. In this work, we propose a unified robust cascade
regression framework that can handle both images with severe occlusion and
images with large head poses. Specifically, the method iteratively predicts the
landmark occlusions and the landmark locations. For occlusion estimation,
instead of directly predicting the binary occlusion vectors, we introduce a
supervised regression method that gradually updates the landmark visibility
probabilities in each iteration to achieve robustness. In addition, we
explicitly add occlusion pattern as a constraint to improve the performance of
occlusion prediction. For landmark detection, we combine the landmark
visibility probabilities, the local appearances, and the local shapes to
iteratively update their positions. The experimental results show that the
proposed method is significantly better than state-of-the-art works on images
with severe occlusion and images with large head poses. It is also comparable
to other methods on general "in-the-wild" images.
