Abstract
Facial landmark detection usually suffers from the influence by the change of environment, such as pose variation and illumination. We observe that high pose variation is the one most influence the detection accuracy. To tackle the problem of pose variation, we adopt deep learning approach to learn a good regressor and propose a pose-aware CNN to tackle the pose variation. We first develop CNN classifier to classify facial image according to the pose. Next, we develop two CNN to detect the facial landmarks according to the corresponding pose. In addition, we adjust the refinement level by concluding the shape constraint. Our experimental results show that the pose-aware detector performs better than the original landmark detector.