The proliferation of deep learning solutions and the scarcity of large
annotated datasets pose significant challenges in real-world applications.
Various strategies have been explored to overcome this challenge, with data
augmentation (DA) approaches emerging as prominent solutions. DA approaches
involve generating additional examples by transforming existing labeled data,
thereby enriching the dataset and helping deep learning models achieve improved
generalization without succumbing to overfitting. In real applications, where
solutions based on deep learning are widely used, there is facial expression
recognition (FER), which plays an essential role in human communication,
improving a range of knowledge areas (e.g., medicine, security, and marketing).
In this paper, we propose a simple and comprehensive face data augmentation
approach based on mixed face component regularization that outperforms the
classical DA approaches from the literature, including the MixAugment which is
a specific approach for the target task in two well-known FER datasets existing
in the literature.

基于混合面部组件规范化的简单而全面的面部数据增强方法在面部表情识别中优于文献中的经典数据增强方法，包括 MixAugment

FaceMixup: 通过混合面部规范化增强面部表情识别

FaceMixup: Enhancing Facial Expression Recognition through Mixed Face  Regularization

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.

本文从转换类型和方法的角度系统地回顾了现有的人脸数据增强研究，特别关注于基于深度学习的方法，尤其是生成式对抗网络。研究结果表明，这些方法为丰富人脸训练集并提高其质量提供了更有效的工具，并提出了评价它们的指标及未来的挑战。