  Face frontalization provides an effective and efficient way for face data
augmentation and further improves the face recognition performance in extreme
pose scenario. Despite recent advances in deep learning-based face synthesis
approaches, this problem is still challenging due to significant pose and
illumination discrepancy. In this paper, we present a novel Dual-Attention
Generative Adversarial Network (DA-GAN) for photo-realistic face frontalization
by capturing both contextual dependencies and local consistency during GAN
training. Specifically, a self-attention-based generator is introduced to
integrate local features with their long-range dependencies yielding better
feature representations, and hence generate faces that preserve identities
better, especially for larger pose angles. Moreover, a novel
face-attention-based discriminator is applied to emphasize local features of
face regions, and hence reinforce the realism of synthetic frontal faces.
Guided by semantic segmentation, four independent discriminators are used to
distinguish between different aspects of a face (\ie skin, keypoints, hairline,
and frontalized face). By introducing these two complementary attention
mechanisms in generator and discriminator separately, we can learn a richer
feature representation and generate identity preserving inference of frontal
views with much finer details (i.e., more accurate facial appearance and
textures) comparing to the state-of-the-art. Quantitative and qualitative
experimental results demonstrate the effectiveness and efficiency of our DA-GAN
approach.
