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

By Xiang Wang, Kai Wang, Shiguo Lian. 2019

145 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

A Survey of Data Augmentation Approaches for NLP (2021, background): “types of data, such as images (Shorten and Khoshgoftaar, 2019), faces (Wang et al., 2019b), and time series (Iwana and Uchida, 2020).”
Editing in Style: Uncovering the Local Semantics of GANs (2020, background): “For a thorough review of the GAN literature we refer the reader to recent surveys in [7, 13, 27].”
Data Augmentation for Graph Neural Networks (2020, methodology): “Prior CV literature (Wang, Wang, and Lian 2019) considers image data augmentation a two-step process: (1) applying a transformation f : S → T to input images S to generate variants T , and (2) utilizing S ∪ T for model training.”
Image Data Augmentation for Deep Learning: A Survey (2022, background): “[Wang et al. , 2020] reviews existing face data augmentation works from perspectives of the transformation types and deep learning.”
A Comprehensive Survey of Image Augmentation Techniques for Deep Learning (2022, background): “First, we do not conﬁne ourselves to a speciﬁc type of image, such as facial images [8].”
Data Augmentation techniques in time series domain: a survey and taxonomy (2022, background): “Some authors [124] tend to differentiate between DA and data generation due to the great advances made in NN models.”
Generative Adversarial Networks for Face Generation: A Survey (2022, background): “Among popular works, some works [82, 137] showed that adding generated data to a small database increases performance by 15%.”