  In this paper, we propose a novel face alignment method that trains deep
convolutional network from coarse to fine. It divides given landmarks into
principal subset and elaborate subset. We firstly keep a large weight for
principal subset to make our network primarily predict their locations while
slightly take elaborate subset into account. Next the weight of principal
subset is gradually decreased until two subsets have equivalent weights. This
process contributes to learn a good initial model and search the optimal model
smoothly to avoid missing fairly good intermediate models in subsequent
procedures. On the challenging COFW dataset [1], our method achieves 6.33% mean
error with a reduction of 21.37% compared with the best previous result [2].
