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A Survey on Face Age Progression Using Deep Learning

  • Unique Paper ID: 157909
  • Volume: 9
  • Issue: 8
  • PageNo: 200-203
  • 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.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 8
  • PageNo: 200-203

A Survey on Face Age Progression Using Deep Learning

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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