Facial Expression-Aware Face Frontalization

Face frontalization is a rising technique for view-invariant face analysis. It enables a non-frontal facial image to recover its general facial appearances to frontal view. A few pioneering works have been proposed very recently. However, face frontalization with detailed facial expression recovering is still very challenging due to the non-linear relationships between head-pose and expression variations. In this paper, we propose a novel facial expression-aware face frontalization method aiming at reconstructing the frontal view while maintaining vivid appearances with regards to facial expressions. First of all, we design multiple face shape models as the reference templates in order to fit in with various shape of facial expressions. Each template describes a set of typical facial actions referred to Facial Action Coding System (FACS). Then a template matching strategy is applied by measuring a weighted Chi Square error such that the input image can be matched with the most approximate template. Finally, Robust Statistical face Frontalization (RSF) method is employed for the task of frontal view recovery. This method is validated on a spontaneous facial expression database and the experimental results show that the proposed method outperforms the state-of-the-art methods.