Cross-Domain Face Synthesis using a Controllable GAN

Fania Mokhayeri, Kaveh Kamali, Eric Granger; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 252-260

The performance of face recognition (FR) systems for video surveillance has been shown to improve when the design data is augmented through synthetic face generation. This is true, for instance, with pair-wise matchers (e.g., deep Siamese networks) that rely on a reference gallery, typically with one still image per individual. However, generating synthetic images based on stills may not improve performance during operations due to the domain shift w.r.t. the target domain. Moreover, despite the emergence of Generative Adversarial Networks (GANs) for realistic synthetic generation, it is often difficult to control the conditions under which synthetic faces are generated. In this paper, a cross-domain face synthesis approach is proposed that integrates a new Controllable GAN (C-GAN). It employs an off-the-shelf 3D face model as a simulator to generate facial images under various poses. The simulated images and noise are input to the C-GAN for realism refinement. It relies on an additional adversarial game as a third player to preserve the identity and specific facial attributes of the refined images. This allows generating realistic synthetic face images that reflect capture conditions in the target domain, while controlling the GAN output such that faces may be generated under desired pose conditions. Experiments were performed using videos from the Chokepoint and COX-S2V datasets, and a deep Siamese network for FR with a single reference still per person. Results indicate that the proposed approach can provide a higher level of accuracy compared to state-of-the-art approaches for synthetic data augmentation.