  Obtaining a high-quality frontal face image from a low-resolution (LR)
non-frontal face image is primarily important for many facial analysis
applications. However, mainstreams either focus on super-resolving near-frontal
LR faces or frontalizing non-frontal high-resolution (HR) faces. It is
desirable to perform both tasks seamlessly for daily-life unconstrained face
images. In this paper, we present a novel Vivid Face Hallucination Generative
Adversarial Network (VividGAN) for simultaneously super-resolving and
frontalizing tiny non-frontal face images. VividGAN consists of coarse-level
and fine-level Face Hallucination Networks (FHnet) and two discriminators,
i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR
face and then the fine-level FHnet makes use of the facial component appearance
prior, i.e., fine-grained facial components, to attain a frontal HR face image
with authentic details. In the fine-level FHnet, we also design a facial
component-aware module that adopts the facial geometry guidance as clues to
accurately align and merge the frontal coarse HR face and prior information.
Meanwhile, two-level discriminators are designed to capture both the global
outline of a face image as well as detailed facial characteristics. The
Coarse-D enforces the coarsely hallucinated faces to be upright and complete
while the Fine-D focuses on the fine hallucinated ones for sharper details.
Extensive experiments demonstrate that our VividGAN achieves photo-realistic
frontal HR faces, reaching superior performance in downstream tasks, i.e., face
recognition and expression classification, compared with other state-of-the-art
methods.
