  MeshFace photos have been widely used in many Chinese business organizations
to protect ID face photos from being misused. The occlusions incurred by random
meshes severely degenerate the performance of face verification systems, which
raises the MeshFace verification problem between MeshFace and daily photos.
Previous methods cast this problem as a typical low-level vision problem, i.e.
blind inpainting. They recover perceptually pleasing clear ID photos from
MeshFaces by enforcing pixel level similarity between the recovered ID images
and the ground-truth clear ID images and then perform face verification on
them. Essentially, face verification is conducted on a compact feature space
rather than the image pixel space. Therefore, this paper argues that pixel
level similarity and feature level similarity jointly offer the key to improve
the verification performance. Based on this insight, we offer a novel feature
oriented blind face inpainting framework. Specifically, we implement this by
establishing a novel DeMeshNet, which consists of three parts. The first part
addresses blind inpainting of the MeshFaces by implicitly exploiting extra
supervision from the occlusion position to enforce pixel level similarity. The
second part explicitly enforces a feature level similarity in the compact
feature space, which can explore informative supervision from the feature space
to produce better inpainting results for verification. The last part copes with
face alignment within the net via a customized spatial transformer module when
extracting deep facial features. All the three parts are implemented within an
end-to-end network that facilitates efficient optimization. Extensive
experiments on two MeshFace datasets demonstrate the effectiveness of the
proposed DeMeshNet as well as the insight of this paper.
