DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification

doi:10.1109/TIFS.2017.2763119

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
	Zhang, Shu1 ; He, Ran1,2 ; Sun, Zhenan1,2 ; Tan, Tieniu1,2
刊名	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
	2018-03-01
卷号	13 期号:3 页码:637-647
关键词	Meshface Face Verification Blind Inpainting Deep Learning Demeshnet Spatial Transformer
DOI	10.1109/TIFS.2017.2763119
文献子类	Article
英文摘要	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.
WOS关键词	REMOVAL ; RAIN
WOS研究方向	Computer Science ; Engineering
语种	英语
WOS记录号	WOS:000418723000007
资助机构	National Natural Science Foundation of China(61622310 ; Beijing Municipal Science and Technology Commission(Z161100000216144) ; State Key Development Program(2016YFB1001001) ; 61473289)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/21928]
专题	自动化研究所_智能感知与计算研究中心
作者单位	1.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Shu,He, Ran,Sun, Zhenan,et al. DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2018,13(3):637-647.
APA	Zhang, Shu,He, Ran,Sun, Zhenan,&Tan, Tieniu.(2018).DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,13(3),637-647.
MLA	Zhang, Shu,et al."DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 13.3(2018):637-647.