Eye Gaze Correction Using Generative Adversarial Networks

被引：0

作者：

Yamamoto, Takahiko ^{[1
]}

Seo, Masataka ^{[1
]}

Kitajima, Toshihiko ^{[2
]}

Chen, Yen-Wei ^{[1
]}

机构：

[1] Ritsumeikan Univ, Grad Sch Informat Sci & Engn, Kusatsu, Shiga, Japan

[2] Sumsung R&D Inst Japan, Osaka, Japan

来源：

2018 IEEE 7TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS (GCCE 2018) | 2018年

关键词：

deep learning; image-to-image translation; gaze correction; Generative Adversarial Net(GAN); Conditional GAN;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Eye gaze correction is an important topic in video teleconference and video chart in order to keep the eye contact. In this paper, we propose to use a generative adversarial networks for eye gaze correction. We use pairs of front facial image (idea camera setting) and real facial image (real camera setting) to training the network. By using the trained network, we can generate a gaze corrected facial image (front facial image) for any real facial image. Experiments demonstrated the effectiveness of our proposed method.

引用

页码：276 / 277

页数：2

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