3D Face Image Inpainting with Generative Adversarial Nets
Joint Authors
Wei, Tongxin
Li, Qingbao
Liu, Jinjin
Zhang, Ping
Chen, Zhifeng
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-14
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In the process of face recognition, face acquisition data is seriously distorted.
Many face images collected are blurred or even missing.
Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets.
In this paper, we propose a 3D face image inpainting method based on generative adversarial nets.
We identify two parallels of the vector to locate the planer positions.
Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect.
We make the face recognition performance dramatically boost.
American Psychological Association (APA)
Wei, Tongxin& Li, Qingbao& Liu, Jinjin& Zhang, Ping& Chen, Zhifeng. 2020. 3D Face Image Inpainting with Generative Adversarial Nets. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1201831
Modern Language Association (MLA)
Wei, Tongxin…[et al.]. 3D Face Image Inpainting with Generative Adversarial Nets. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1201831
American Medical Association (AMA)
Wei, Tongxin& Li, Qingbao& Liu, Jinjin& Zhang, Ping& Chen, Zhifeng. 3D Face Image Inpainting with Generative Adversarial Nets. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1201831
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201831