Frontiers of Data and Computing ›› 2023, Vol. 5 ›› Issue (2): 2-23.

CSTR: 32002.14.jfdc.CN10-1649/TP.2023.02.001

doi: 10.11871/jfdc.issn.2096-742X.2023.02.001

• Special Issue: AI for Science • Previous Articles     Next Articles

Face Age Editing Methods Based on Generative Adversarial Network: A Survey

LIU Yunfan1,2(),LI Qi2,SUN Zhenan1,2,*(),TAN Tieniu1,2   

  1. 1. School of Artificial Intellignence, University of Chinese Academy of Sciences, Beijing 100049, China
    2. Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2023-01-28 Online:2023-04-20 Published:2023-04-24
  • Contact: SUN Zhenan;


[Objective] This paper provides a comprehensive and systematic review of the face age editing methods based on generative adversarial network. [Coverage] This survey contains numerous studies including publications in top-tier international journals and conferences, as well as other representative works in this field. [Methods] From the perspective of the internal mechanism of generative adversarial networks, these studies are categorized according to how the latent vector is obtained and manipulated, and a brief analysis and comparison of the similarity and difference of specific models is provided. [Results] With the rapid development of generative adversarial networks, the performance of the face age editing method in all aspects has been continuously and steadily improved, while there are still challenges and problems in various aspects. [Limitations] Due to the lack of a developed and mature evaluation standard, it is temporarily impossible for this paper to objectively measure and compare the discussed methods with a set of unified quantitative metrics. [Conclusions] Although significant outcome has been achieved, existing methods still face unsolved problems, including the flexibility of the editing process, the adaptability to extreme editing cases, and the diversity of generation results.

Key words: deep learning, generative adversarial network, image translation, facial attribute, face age editing