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.

中文翻译：

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用于深度神经网络训练的面部数据增强调查

摘要

训练集的质量和规模对基于深度学习的面部相关任务的结果有很大影响。但是，以高质量和均衡的分布来收集和标记足够的样本仍然是一项艰巨且昂贵的工作，因此各种数据增强技术已被广泛用于丰富训练数据集。在本文中，我们从转换类型和方法的角度回顾了现有的面部数据增强工作，并采用了最新技术。在所有这些方法中，我们将重点放在基于深度学习的作品上，尤其是生成对抗网络，这些网络近年来被认为是更强大和有效的工具。我们介绍了它们的原理，讨论了结果，并说明了它们的应用和局限性。还介绍了用于评估这些方法的不同评估指标。我们指出了面部数据增强领域的挑战和机遇，并提供了简短而有见地的讨论。