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journal article Dec 03, 2022

Generative Adversarial Networks for Face Generation: A Survey

Amina Kammoun ORCID Rim Slama ORCID Hedi Tabia ORCID Tarek Ouni ORCID Mohmed Abid ORCID
ACM Computing Surveys Vol. 55 No. 5 pp. 1-37 · Association for Computing Machinery (ACM)
View at Publisher Save 10.1145/3527850
Abstract
Recently, generative adversarial networks (GANs) have progressed enormously, which makes them able to learn complex data distributions in particular faces. More and more efficient GAN architectures have been designed and proposed to learn the different variations of faces, such as cross pose, age, expression, and style. These GAN-based approaches need to be reviewed, discussed, and categorized in terms of architectures, applications, and metrics. Several reviews that focus on the use and advances of GAN in general have been proposed. However, to the best of our knowledge, the GAN models applied to the face, which we call
facial GANs
, have never been addressed. In this article, we review facial GANs and their different applications. We mainly focus on architectures, problems, and performance evaluation with respect to each application and used datasets. More precisely, we review the progress of architectures and discuss the contributions and limits of each. Then, we expose the encountered problems of facial GANs and propose solutions to handle them. Additionally, as GAN evaluation has become a notable current defiance, we investigate the state-of-the-art quantitative and qualitative evaluation metrics and their applications. We conclude this work with a discussion on the face generation challenges and propose open research issues.
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Metrics
123
Citations
169
References
Details
Published
Dec 03, 2022
Vol/Issue
55(5)
Pages
1-37
License
View
Authors
A
Amina Kammoun

University of Sfax, Sfax, Tunisia

R
Rim Slama

CESI Lyon, Lyon, France

H
Hedi Tabia

Université Paris-Saclay, Paris, France

T
Tarek Ouni

University of Sfax, Sfax, Tunisia

M
Mohmed Abid

University of Sfax, Sfax, Tunisia

Cite This Article
Amina Kammoun, Rim Slama, Hedi Tabia, et al. (2022). Generative Adversarial Networks for Face Generation: A Survey. ACM Computing Surveys, 55(5), 1-37. https://doi.org/10.1145/3527850
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