DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification | |
Zhang, Shu1; He, Ran1,2; Sun, Zhenan1,2; Tan, Tieniu1,2 | |
发表期刊 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY |
2018-03-01 | |
卷号 | 13期号:3页码:637-647 |
文章类型 | Article |
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. |
关键词 | Meshface Face Verification Blind Inpainting Deep Learning Demeshnet Spatial Transformer |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIFS.2017.2763119 |
关键词[WOS] | REMOVAL ; RAIN |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | National Natural Science Foundation of China(61622310 ; Beijing Municipal Science and Technology Commission(Z161100000216144) ; State Key Development Program(2016YFB1001001) ; 61473289) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000418723000007 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/21928 |
专题 | 智能感知与计算 |
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 |
第一作者单位 | 模式识别国家重点实验室 |
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. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论