# A Survey on Face Data Augmentation

arXiv: https://arxiv.org/abs/1904.11685 · HTML: https://mlsanity.com/paper/1904.11685/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 23 of 23)

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- [On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study](https://mlsanity.com/paper/2104.02653/) (2021)
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- [CantorNet: A Sandbox for Testing Geometrical and Topological Complexity Measures](https://mlsanity.com/paper/2411.19713/) (2024)
- [Advancing Marine Bioacoustics with Deep Generative Models: A Hybrid Augmentation Strategy for Southern Resident Killer Whale Detection](https://mlsanity.com/paper/2511.21872/) (2025)
- [Two-Step Data Augmentation for Masked Face Detection and Recognition: Turning Fake Masks to Real](https://mlsanity.com/paper/2512.15774/) (2025)
- [ForensicNet: Lightweight Attention-Enhanced MobileNetV2 for Automated Face Identification](https://mlsanity.com/paper/2607.16273/) (2026)

## Abstract (arXiv)

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.