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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Review of face attribute synthesis techniques based on generative adversarial networks

Wang Jianqiang1
Zhang Ke1,2
Li Peijie1
1. Dept. of Electronic & Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, China
2. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, Hebei, China
DOI: 10.19734/j.issn.1001-3695.2024.05.0240

Abstract

Face attribute synthesis technology aims to reconstruct face attributes according to the specified target while retaining the identity information of face images. The development of computer vision technology has provided a new solution for face attribute synthesis technology. To this end, this paper focuses on the face attribute synthesis dataset, traditional and generative adversarial network (GAN) synthesis networks, and a review of face semantics. The development of facial attribute synthesis technology. First, this paper analyzes the traditional methods and mainstream deep learning methods in the field of facial attribute synthesis, explores the development status of GAN-based methods, divides GAN-based facial attribute synthesis models into supervised, unsupervised and semi-supervised, and divides facial attributes into three semantic categories of age, expression, and makeup, and conducts in-depth research on multiple synthesis models. Secondly, this paper analyzes and summarizes typical loss functions. At the same time, this paper introduces the commonly used facial attribute datasets and evaluation indicators. Finally, this paper introduces the problems with existing face attribute synthesis methods and proposes prospects for the future development of this field.

Foundation Support

国家自然科学基金资助项目(62076093,62206095,61871182)
中央高校基本科研业务费专项资金资助项目(2023JG002,2022MS078,2023JC006)
河北省省级科技计划资助项目(SZX2020034)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.05.0240
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-03] Accepted Paper

Cite This Article

王健强, 张珂, 李培杰. 基于生成对抗网络的人脸属性合成技术综述 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0240. (Wang Jianqiang, Zhang Ke, Li Peijie. Review of face attribute synthesis techniques based on generative adversarial networks [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.05.0240. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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