Eye Gaze Correction Using Generative Adversarial Networks
Takahiko Yamamoto, Masataka Seo, Toshihiko Kitajima, Yen‐Wei Chen · 2018
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.