  The quality and size of training set have great impact on the results of deep
learning-based face related tasks. However, collecting and labeling adequate
samples with high quality and balanced distributions still remains a laborious
and expensive work, and various data augmentation techniques have thus been
widely used to enrich the training dataset. In this paper, we systematically
review the existing works of face data augmentation from the perspectives of
the transformation types and methods, with the state-of-the-art approaches
involved. Among all these approaches, we put the emphasis on the deep
learning-based works, especially the generative adversarial networks which have
been recognized as more powerful and effective tools in recent years. We
present their principles, discuss the results and show their applications as
well as limitations. Different evaluation metrics for evaluating these
approaches are also introduced. We point out the challenges and opportunities
in the field of face data augmentation, and provide brief yet insightful
discussions.
