  The recent progress of CNN has dramatically improved face alignment
performance. However, few works have paid attention to the error-bias with
respect to error distribution of facial landmarks. In this paper, we
investigate the error-bias issue in face alignment, where the distributions of
landmark errors tend to spread along the tangent line to landmark curves. This
error-bias is not trivial since it is closely connected to the ambiguous
landmark labeling task. Inspired by this observation, we seek a way to leverage
the error-bias property for better convergence of CNN model. To this end, we
propose anisotropic direction loss (ADL) and anisotropic attention module (AAM)
for coordinate and heatmap regression, respectively. ADL imposes strong binding
force in normal direction for each landmark point on facial boundaries. On the
other hand, AAM is an attention module which can get anisotropic attention mask
focusing on the region of point and its local edge connected by adjacent
points, it has a stronger response in tangent than in normal, which means
relaxed constraints in the tangent. These two methods work in a complementary
manner to learn both facial structures and texture details. Finally, we
integrate them into an optimized end-to-end training pipeline named ADNet. Our
ADNet achieves state-of-the-art results on 300W, WFLW and COFW datasets, which
demonstrates the effectiveness and robustness.
