  This paper investigates how to rapidly and accurately localize facial
landmarks in unconstrained, cluttered environments rather than in the well
segmented face images. We present a novel Backbone-Branches Fully-Convolutional
Neural Network (BB-FCN), which produces facial landmark response maps directly
from raw images without relying on pre-process or sliding window approaches.
BB-FCN contains one backbone and a number of network branches with each
corresponding to one landmark type, and it operates in a progressive manner.
Specifically, the backbone roughly detects the locations of facial landmarks by
taking the whole image as input, and the branches further refine the
localizations based on a local observation from the backbone's intermediate
feature map. Moreover, our backbone-branches architecture does not contain
full-connection layers for location regression, leading to efficient learning
and inference. Our extensive experiments show that our model achieves superior
performances over other state-of-the-arts under both the constrained (i.e. with
face regions) and the "in the wild" scenarios.
