  Facial expression editing is a challenging task as it needs a high-level
semantic understanding of the input face image. In conventional methods, either
paired training data is required or the synthetic face resolution is low.
Moreover, only the categories of facial expression can be changed. To address
these limitations, we propose an Expression Generative Adversarial Network
(ExprGAN) for photo-realistic facial expression editing with controllable
expression intensity. An expression controller module is specially designed to
learn an expressive and compact expression code in addition to the
encoder-decoder network. This novel architecture enables the expression
intensity to be continuously adjusted from low to high. We further show that
our ExprGAN can be applied for other tasks, such as expression transfer, image
retrieval, and data augmentation for training improved face expression
recognition models. To tackle the small size of the training database, an
effective incremental learning scheme is proposed. Quantitative and qualitative
evaluations on the widely used Oulu-CASIA dataset demonstrate the effectiveness
of ExprGAN.
