  Data augmentation has been highly effective in narrowing the data gap and
reducing the cost for human annotation, especially for tasks where ground truth
labels are difficult and expensive to acquire. In face recognition, large pose
and illumination variation of face images has been a key factor for performance
degradation. However, human annotation for the various face understanding tasks
including face landmark localization, face attributes classification and face
recognition under these challenging scenarios are highly costly to acquire.
Therefore, it would be desirable to perform data augmentation for these cases.
But simple 2D data augmentation techniques on the image domain are not able to
satisfy the requirement of these challenging cases. As such, 3D face modeling,
in particular, single image 3D face modeling, stands a feasible solution for
these challenging conditions beyond 2D based data augmentation. To this end, we
propose a method that produces realistic 3D augmented images from multiple
viewpoints with different illumination conditions through 3D face modeling,
each associated with geometrically accurate face landmarks, attributes and
identity information. Experiments demonstrate that the proposed 3D data
augmentation method significantly improves the performance and robustness of
various face understanding tasks while achieving state-of-arts on multiple
benchmarks.
