  Registering a 3D facial model to a 2D image under occlusion is difficult.
First, not all of the detected facial landmarks are accurate under occlusions.
Second, the number of reliable landmarks may not be enough to constrain the
problem. We propose a method to synthesize additional points (SensiblePoints)
to create pose hypotheses. The visual clues extracted from the fiducial points,
non-fiducial points, and facial contour are jointly employed to verify the
hypotheses. We define a reward function to measure whether the projected dense
3D model is well-aligned with the confidence maps generated by two fully
convolutional networks, and use the function to train recurrent policy networks
to move the SensiblePoints. The same reward function is employed in testing to
select the best hypothesis from a candidate pool of hypotheses. Experimentation
demonstrates that the proposed approach is very promising in solving the facial
model registration problem under occlusion.
