  Recently, Deep Convolution Networks (DCNNs) have been applied to the task of
face alignment and have shown potential for learning improved feature
representations. Although deeper layers can capture abstract concepts like
pose, it is difficult to capture the geometric relationships among the
keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution
network for facial keypoint detection. Our model predicts the 2D locations of
the keypoints and their individual visibility along with 3D head pose, while
exploiting the spatial relationships among different keypoints. Different from
existing approaches of modeling these relationships, we propose learnable
transform functions which captures the relationships between keypoints at
feature level. However, due to extensive variations in pose, not all of these
relationships act at once, and hence we propose, a pose-based routing function
which implicitly models the active relationships. Both transform functions and
the routing function are implemented through convolutions in a multi-task
framework. Our approach presents a single-shot keypoint detection method,
making it different from many existing cascade regression-based methods. We
also show that learning these relationships significantly improve the accuracy
of keypoint detections for in-the-wild face images from challenging datasets
such as AFW and AFLW.
