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A survey on face data augmentation for the training of deep neural networks

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Abstract

The quality and size of training set have a 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 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.

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Authors and Affiliations

  1. CloudMinds Technologies Inc., Beijing, China

    Xiang Wang & Kai Wang

  2. China Unicom, Beijing, China

    Shiguo Lian

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  1. Xiang Wang
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  2. Kai Wang
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Appendix

Appendix

A summary of recent works on face data augmentation is illustrated in the following table (Table 3). It summarizes these works from the transformation type, method and the evaluations they performed to test the capability of their algorithms for data augmentation. There is one notable thing that we only label the transformation types explicitly mentioned in original papers. Maybe the methods have the capability to be used for other transformations, but the authors did not mention in their original paper.

Table 3 Summary of recent works
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Wang, X., Wang, K. & Lian, S. A survey on face data augmentation for the training of deep neural networks. Neural Comput & Applic 32, 15503–15531 (2020). https://doi.org/10.1007/s00521-020-04748-3

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