  Facial landmark localization plays an important role in face recognition and
analysis applications. In this paper, we give a brief introduction to a
coarse-to-fine pipeline with neural networks and sequential regression. First,
a global convolutional network is applied to the holistic facial image to give
an initial landmark prediction. A pyramid of multi-scale local image patches is
then cropped to feed to a new network for each landmark to refine the
prediction. As the refinement network outputs a more accurate position
estimation than the input, such procedure could be repeated several times until
the estimation converges. We evaluate our system on the 300-W dataset [11] and
it outperforms the recent state-of-the-arts.
