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DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
Shu Zhang1; Ran He1,2; Zhenan Sun1,2; Tieniu Tan1,2
Source PublicationIEEE Transactions on Information Forensics and Security
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 three parts are implemented within
an end-to-end network that facilitates efficient optimization.
Extensive experiments on two MeshFace data sets demonstrate
the effectiveness of the proposed DeMeshNet as well as the insight
of this paper.
KeywordMeshface Face Verification Blind Inpainting Deep Learning Demeshnet Spatial Transformer
Indexed BySCI
Document Type期刊论文
Corresponding AuthorRan He
Affiliation1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Shu Zhang,Ran He,Zhenan Sun,et al. DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification[J]. IEEE Transactions on Information Forensics and Security,2018,13(3):637-647.
APA Shu Zhang,Ran He,Zhenan Sun,&Tieniu Tan.(2018).DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification.IEEE Transactions on Information Forensics and Security,13(3),637-647.
MLA Shu Zhang,et al."DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification".IEEE Transactions on Information Forensics and Security 13.3(2018):637-647.
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