  The performance of face recognition (FR) systems applied in 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 typically rely on a reference
gallery with one still image per individual. However, generating synthetic
images in the source domain may not improve the 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 face images under
various poses. The simulated images and noise are input to the C-GAN for
realism refinement which employs 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 reflects
capture conditions in the target domain while controlling the GAN output to
generate faces 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 the current
state-of-the-art approaches for synthetic data augmentation.
