  Top-performing landmark estimation algorithms are based on exploiting the
excellent ability of large convolutional neural networks (CNNs) to represent
local appearance. However, it is well known that they can only learn weak
spatial relationships. To address this problem, we propose a model based on the
combination of a CNN with a cascade of Graph Attention Network regressors. To
this end, we introduce an encoding that jointly represents the appearance and
location of facial landmarks and an attention mechanism to weigh the
information according to its reliability. This is combined with a multi-task
approach to initialize the location of graph nodes and a coarse-to-fine
landmark description scheme. Our experiments confirm that the proposed model
learns a global representation of the structure of the face, achieving top
performance in popular benchmarks on head pose and landmark estimation. The
improvement provided by our model is most significant in situations involving
large changes in the local appearance of landmarks.
