A Survey on Face Age Progression Using Deep Learning

A Survey on Face Age Progression Using Deep Learning
Author(s):
Vaishnavi M, Varshitha B S, Theekshana V, Raunak Kumari, Manasa Sandeep
Keywords:
Divide and conquer, image translation, Face aging, Generative adversarial networks and progressive neural networks
Abstract
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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