Automatic Beautification for Group-Photo Facial Expressions Using Novel Bayesian GANs

Abstract

Directly benefiting from the powerful generative adversarial networks (GANs) in recent years, various new image processing tasks pertinent to image generation and synthesis have gained more popularity with the growing success. One such application is individual portrait photo beautification based on facial expression detection and editing. Yet, automatically beautifying group photos without tedious and fragile human interventions still remains challenging. The difficulties inevitably arise from diverse facial expression evaluation, harmonious expression generation, and context-sensitive synthesis from single/multiple photos. To ameliorate, we devise a two-stage deep network for automatic group-photo evaluation and beautification by seamless integration of multi-label CNN with Bayesian network enhanced GANs. First, our multi-label CNN is designed to evaluate the quality of facial expressions. Second, our novel Bayesian GANs framework is proposed to automatically generate photo-realistic beautiful expressions. Third, to further enhance naturalness of beautified group photos, we embed Poisson fusion in the final layer of the GANs in order to synthesize all the beautified individual expressions. We conducted extensive experiments on various kinds of single-/multi-frame group photos to validate our novel network design. All the experiments confirm that, our novel method can uniformly accommodate diverse expression evaluation and generation/synthesis of group photos, and outperform the state-of-the-art methods in terms of effectiveness, versatility, and robustness.