Face image synthesis with weight and age progression using conditional adversarial autoencoder

Muhammad Anwaar, Chu Kiong Loo, Manjeevan Seera

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

The appearance of a human face changes with the change in body weight and age. With varying lifestyle choices, it is hard to imagine the appearance of a given human face in years to come. Future self-perception is highly associated with one’s emotional state, as well as health behavior. Negative future self-perception can cause negative lifestyle choice and negative health behavior, leading to depression and eating disorder. In this paper, a new methodology is introduced for future self-face image synthesis using age and weight, resulting in visualization of future face image derived from given weight category and age. A Constrained Local Model is first used for weight progressed future face image synthesized and then age-progressed future face image is generated using Conditional Adversarial Auto Encoder. In the final step, both weight progressed and age-progressed face images fed to face morphing module which synthesized future face image by keeping natural looks. Experimental results show the advantages of proposed method with promising results.

Original languageEnglish
Pages (from-to)3567-3579
Number of pages13
JournalNeural Computing and Applications
Volume32
Issue number8
DOIs
Publication statusPublished - Apr 2020
Externally publishedYes

Keywords

  • Age progression
  • Face simulation
  • Face synthesis
  • Weight synthesis

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