  Advances in face rotation, along with other face-based generative tasks, are
more frequent as we advance further in topics of deep learning. Even as
impressive milestones are achieved in synthesizing faces, the importance of
preserving identity is needed in practice and should not be overlooked. Also,
the difficulty should not be more for data with obscured faces, heavier poses,
and lower quality. Existing methods tend to focus on samples with variation in
pose, but with the assumption data is high in quality. We propose a generative
adversarial network (GAN) -based model to generate high-quality, identity
preserving frontal faces from one or multiple low-resolution (LR) faces with
extreme poses. Specifically, we propose SuperFront-GAN (SF-GAN) to synthesize a
high-resolution (HR), frontal face from one-to-many LR faces with various poses
and with the identity-preserved. We integrate a super-resolution (SR) side-view
module into SF-GAN to preserve identity information and fine details of the
side-views in HR space, which helps model reconstruct high-frequency
information of faces (i.e., periocular, nose, and mouth regions). Moreover,
SF-GAN accepts multiple LR faces as input, and improves each added sample. We
squeeze additional gain in performance with an orthogonal constraint in the
generator to penalize redundant latent representations and, hence, diversify
the learned features space. Quantitative and qualitative results demonstrate
the superiority of SF-GAN over others.
