Automatic Generation of Facial Expression Using Generative Adversarial Nets

With the spread of digital cameras, smart phones, and SNS, the number facial images of people have increased. Facial expression generation from a single facial image has been widely applied to the fields of entertainment and social communication. Many approaches that apply machine learning techniques have been developed. In our previous study, we developed a makeup simulator system. However, this system is incapable of changing the impression of a cosmetic face based on changes in facial expression; in addition, another challenge is that the user cannot see the impression of makeup dynamically and objectively. Therefore, in this study, we generate static facial expression images from a natural (expressionless) image by using generative adversarial networks, which is critical to the research on dynamic facial expression change. Our experimental results demonstrate that our approach achieves the best expression image.