A Survey on Face Age Progression Using Deep Learning
Vaishnavi M, Varshitha B S, Theekshana V, Raunak Kumari, Manasa Sandeep
Divide and conquer, image translation, Face aging, Generative adversarial networks and progressive neural networks
Face aging progression (FAP) refers to synthesizing facial images while simulating aging to predict a person's future appearance.The generation of age-related facial images benefits a wide range of applications, including facial recognition systems, forensic investigations, and digital entertainment. In particular, recent successes achieved with deep generative networks have significantly improved the quality of age-synthesized facial images in terms of visual fidelity, aging accuracy, and identity preservation. However, a large number of recent contributions require systematic structuring of new discoveries and ideas to identify common taxonomies, speed up future research, and reduce redundancy. FAP is translation-based, conditional-based, and sequence-based. In addition, we provide a comprehensive overview of the most common performance assessment techniques to steer future research in the right direction.
Article Details
Unique Paper ID: 157909

Publication Volume & Issue: Volume 9, Issue 8

Page(s): 200 - 203
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