Face age progression and regression based on various types of GANS

Abstract

Recently, there has been a surge of interest among scientists in the application of face age progression and regression, spanning various fields such as criminal investigation and archaeology. Simultaneously, the computer world has been buzzing with excitement over Generative Adversarial Networks (GANs), thanks to their remarkable efficiency and adaptability. Within this context, researchers have successfully harnessed the power of GANs to develop methods for face age progression and regression. Each of these approaches boasts its unique model and architecture, equipping them with distinct sets of limitations and advantages. This article provides a comprehensive review of the methods of implementing face age progression and regression by GANs. To be specific, this paper mainly talks about Wavelet-based GANs and Identity-Preserved cGANs. For each method, the author introduces its basic idea and explains its framework and special parts in detail. The outputs of each model and their characteristics and limitations are also concluded in the discussion. Besides, this paper also describes two real-life applications of this technology, including finding lost children and predicting results after cosmetic surgeries. The introduction of the practical applications provides possible directions for researchers to combine different types of GANS with face age progression and regression in the near future.