Face information forensics analysis based on facial aging: A Survey

Abstract

Face recognition systems are now confronted with the problem of “aging”. The difficulty arises from age-related biological changes, which might result in considerable differences in face traits between two photographs obtained at various ages of the same individual. Because the face is the area of the body that is most impacted by aging, the extraction of strong facial traits for age-invariant face recognition is becoming increasingly important, especially when there are huge age disparities between the same person’s face photos. Face Age Progression (FAP) is the process of synthesizing face photos while simulating aging processes to anticipate an individual’s future look. The production of age-progressed face photographs has advantages for a variety of applications; Face recognition methods, private investigators, and entertainment content are just a few examples, particularly for recognizing and protecting missing abducted children using childhood photographs or Alzheimer’s people. Deep generative networks’ success recently, in particular, has considerably improved the maturity level of face’s qualities pictures about visual clarity, aging correctness, as well as identity preservation. This paper present a comparison of contemporary approaches to face age growth deep learning-based for both adulthood and youth face aged, face age progression FAP is classified into three rising concepts: translation-based, condition-based, & sequence-based. This paper provides a complete overview of one of the most frequently used methods of achievement assessment and a comprehensive list of available datasets.