  Facial landmark localisation in images captured in-the-wild is an important
and challenging problem. The current state-of-the-art revolves around certain
kinds of Deep Convolutional Neural Networks (DCNNs) such as stacked U-Nets and
Hourglass networks. In this work, we innovatively propose stacked dense U-Nets
for this task. We design a novel scale aggregation network topology structure
and a channel aggregation building block to improve the model's capacity
without sacrificing the computational complexity and model size. With the
assistance of deformable convolutions inside the stacked dense U-Nets and
coherent loss for outside data transformation, our model obtains the ability to
be spatially invariant to arbitrary input face images. Extensive experiments on
many in-the-wild datasets, validate the robustness of the proposed method under
extreme poses, exaggerated expressions and heavy occlusions. Finally, we show
that accurate 3D face alignment can assist pose-invariant face recognition
where we achieve a new state-of-the-art accuracy on CFP-FP.
