Kale, AyseAltun, Oguz2024-03-132024-03-1320230031-32031873-5142https://doi.org/10.1016/j.patcog.2023.109791https://hdl.handle.net/20.500.12662/4255Face age synthesis is the determination of how a person looks in the future or the past by reconstructing their facial image. Determining the change in the human face over the years is a critical process for cross-age face recognition systems in forensic issues such as finding missing people and fugitive criminals. Therefore, it is a subject that has attracted attention in recent years. With the implementation of deep learning methods, better quality and photo-realistic images began to be produced. However, researchers continue to improve both aging accuracy and identity preservation requirements. We group the studies in the literature under two categories: classical methods and deep learning methods. We review both categories in the methods used, evaluation methods, and databases.& COPY; 2023 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessAge progressionAge regressionFace agingGANsReview Article10.1016/j.patcog.2023.1097912-s2.0-85165127841Q1143WOS:001041500500001Q1