  In this paper, we examine 3 important issues in the practical use of
state-of-the-art facial landmark detectors and show how a combination of
specific architectural modifications can directly improve their accuracy and
temporal stability. First, many facial landmark detectors require face
normalization as a preprocessing step, which is accomplished by a
separately-trained neural network that crops and resizes the face in the input
image. There is no guarantee that this pre-trained network performs the optimal
face normalization for landmark detection. We instead analyze the use of a
spatial transformer network that is trained alongside the landmark detector in
an unsupervised manner, and jointly learn optimal face normalization and
landmark detection. Second, we show that modifying the output head of the
landmark predictor to infer landmarks in a canonical 3D space can further
improve accuracy. To convert the predicted 3D landmarks into screen-space, we
additionally predict the camera intrinsics and head pose from the input image.
As a side benefit, this allows to predict the 3D face shape from a given image
only using 2D landmarks as supervision, which is useful in determining landmark
visibility among other things. Finally, when training a landmark detector on
multiple datasets at the same time, annotation inconsistencies across datasets
forces the network to produce a suboptimal average. We propose to add a
semantic correction network to address this issue. This additional lightweight
neural network is trained alongside the landmark detector, without requiring
any additional supervision. While the insights of this paper can be applied to
most common landmark detectors, we specifically target a recently-proposed
continuous 2D landmark detector to demonstrate how each of our additions leads
to meaningful improvements over the state-of-the-art on standard benchmarks.
