  In this paper, we propose a fast and accurate coordinate regression method
for face alignment. Unlike most existing facial landmark regression methods
which usually employ fully connected layers to convert feature maps into
landmark coordinate, we present a structure coherence component to explicitly
take the relation among facial landmarks into account. Due to the geometric
structure of human face, structure coherence between different facial parts
provides important cues for effectively localizing facial landmarks. However,
the dense connection in the fully connected layers overuses such coherence,
making the important cues unable to be distinguished from all connections.
Instead, our structure coherence component leverages a dynamic sparse graph
structure to passing features among the most related landmarks. Furthermore, we
propose a novel objective function, named Soft Wing loss, to improve the
accuracy. Extensive experiments on three popular benchmarks, including WFLW,
COFW and 300W, demonstrate the effectiveness of the proposed method, achieving
state-of-the-art performance with fast speed. Our approach is especially robust
to challenging cases resulting in impressively low failure rate (0% and 2.88%)
in COFW and WFLW datasets.
