survey

Generative Adversarial Networks for Face Generation: A Survey

Authors:

Mohmed AbidAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 5

Article No.: 94, Pages 1 - 37

https://doi.org/10.1145/3527850

Published: 03 December 2022 Publication History

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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Index Terms

Generative Adversarial Networks for Face Generation: A Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 5

May 2023

810 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3567470

Editor:
Albert Zomaya
University of Sydney, Australia

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Published: 03 December 2022

Accepted: 20 March 2022

Revised: 17 March 2022

Received: 11 July 2021

Published in CSUR Volume 55, Issue 5

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