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Data augmentation has been highly effective in narrowing the data gap andreducing the cost for human annotation, especially for tasks where ground truthlabels are difficult and expensive to acquire. In face recognition, large poseand illumination variation of face images has been a key factor for performancedegradation. However, human annotation for the various face understanding tasksincluding face landmark localization, face attributes classification and facerecognition 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 tosatisfy the requirement of these challenging cases. As such, 3D face modeling,in particular, single image 3D face modeling, stands a feasible solution forthese challenging conditions beyond 2D based data augmentation. To this end, wepropose a method that produces realistic 3D augmented images from multipleviewpoints with different illumination conditions through 3D face modeling,each associated with geometrically accurate face landmarks, attributes andidentity information. Experiments demonstrate that the proposed 3D dataaugmentation method significantly improves the performance and robustness ofvarious face understanding tasks while achieving state-of-arts on multiplebenchmarks.

3D-Aided Data Augmentation for Robust Face Understanding