Recursive Chaining of Reversible Image-to-Image Translators for Face Aging

Ari Heljakka*, Arno Solin, Juho Kannala

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

4 Citations (Scopus)
194 Downloads (Pure)


This paper addresses the modeling and simulation of progressive changes over time, such as human face aging. By treating the age phases as a sequence of image domains, we construct a chain of transformers that map images from one age domain to the next. Leveraging recent adversarial image translation methods, our approach requires no training samples of the same individual at different ages. Here, the model must be flexible enough to translate a child face to a young adult, and all the way through the adulthood to old age. We find that some transformers in the chain can be recursively applied on their own output to cover multiple phases, compressing the chain. The structure of the chain also unearths information about the underlying physical process. We demonstrate the performance of our method with precise and intuitive metrics, and visually match with the face aging state-of-the-art.

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings
Number of pages12
ISBN (Print)9783030014483
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Conference publication
EventInternational Conference on Advanced Concepts for Intelligent Vision Systems - Poitiers, France
Duration: 24 Sept 201827 Sept 2018
Conference number: 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11182 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Advanced Concepts for Intelligent Vision Systems
Abbreviated titleACIVS


  • Deep learning
  • Face aging
  • Face synthesis
  • GAN
  • Transfer learning


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