A Survey on Face Data Augmentation

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.

arXiv comment26 pages, 22 figures. Neural Comput & Applic (2020)

References in corpus (6)

• FaceNet: A Unified Embedding for Face Recognition and Clustering
• Smart Augmentation - Learning an Optimal Data Augmentation Strategy
• Sample-to-Sample Correspondence for Unsupervised Domain Adaptation
• Unsupervised Domain Adaptation using Regularized Hyper-graph Matching
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Cited by in corpus (6)

• A Comprehensive Survey of Image Augmentation Techniques for Deep Learning
• Data Augmentation techniques in time series domain: A survey and taxonomy
• A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
• A survey of synthetic data augmentation methods in computer vision
• On the Pitfalls of Learning with Limited Data: A Facial Expression Recognition Case Study
• Landmarks Augmentation with Manifold-Barycentric Oversampling