  In this paper, we focus on the facial expression translation task and propose
a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one
image domain to another one based on an additional expression attribute. The
proposed ECGAN is a generic framework and is applicable to different expression
generation tasks where specific facial expression can be easily controlled by
the conditional attribute label. Besides, we introduce a novel face mask loss
to reduce the influence of background changing. Moreover, we propose an entire
framework for facial expression generation and recognition in the wild, which
consists of two modules, i.e., generation and recognition. Finally, we evaluate
our framework on several public face datasets in which the subjects have
different races, illumination, occlusion, pose, color, content and background
conditions. Even though these datasets are very diverse, both the qualitative
and quantitative results demonstrate that our approach is able to generate
facial expressions accurately and robustly.
