# A Survey on Face Data Augmentation

## TL;DR

This survey reviews face data augmentation techniques, emphasizing deep learning methods like GANs, discussing their principles, applications, limitations, and evaluation metrics to enhance face recognition tasks.

## Contribution

It provides a comprehensive overview of face data augmentation methods, especially deep learning-based approaches, highlighting recent advances, challenges, and future opportunities.

## Key findings

- GANs are effective in generating diverse face data
- Evaluation metrics vary across augmentation methods
- Deep learning approaches outperform traditional techniques

## Abstract

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.