  Face parsing infers a pixel-wise label to each facial component, which has
drawn much attention recently. Previous methods have shown their efficiency in
face parsing, which however overlook the correlation among different face
regions. The correlation is a critical clue about the facial appearance, pose,
expression etc., and should be taken into account for face parsing. To this
end, we propose to model and reason the region-wise relations by learning graph
representations, and leverage the edge information between regions for
optimized abstraction. Specifically, we encode a facial image onto a global
graph representation where a collection of pixels ("regions") with similar
features are projected to each vertex. Our model learns and reasons over
relations between the regions by propagating information across vertices on the
graph. Furthermore, we incorporate the edge information to aggregate the
pixel-wise features onto vertices, which emphasizes on the features around
edges for fine segmentation along edges. The finally learned graph
representation is projected back to pixel grids for parsing. Experiments
demonstrate that our model outperforms state-of-the-art methods on the widely
used Helen dataset, and also exhibits the superior performance on the
large-scale CelebAMask-HQ and LaPa dataset. The code is available at
https://github.com/tegusi/EAGRNet.
