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Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks. In this study, we review a classic approach that uses a generative adversarial network. The structure, formulation, learning algorithm, challenges, advantages, and disadvantages of the algorithms contained in each proposed algorithm are discussed systematically. Generative Adversarial Networks are an approach that obtains the status of the art in the field of face aging by adding an aging module, paying special attention to the face part, and using an identity-preserving module to preserve identity. In this paper, we also discuss the database used for facial aging, along with its characteristics. The dataset used in the face aging process must have the following criteria: (1) a sufficiently large age group in the dataset, each age group must have a small range, (2) a balanced distribution of each age group, and (3) has enough number of face images.

Recent Generative Adversarial Approach in Face Aging and Dataset Review