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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Details

Original languageEnglish
Title of host publicationAdvanced Concepts for Intelligent Vision Systems - 19th International Conference, ACIVS 2018, Proceedings
PublisherSpringer Verlag
Pages309-320
Number of pages12
ISBN (Print)9783030014483
StatePublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Advanced Concepts for Intelligent Vision Systems - Poitiers, France
Duration: 24 Sep 201827 Sep 2018
Conference number: 19

Publication series

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

Conference

ConferenceInternational Conference on Advanced Concepts for Intelligent Vision Systems
Abbreviated titleACIVS
CountryFrance
CityPoitiers
Period24/09/201827/09/2018

Researchers

Research units

  • GenMind Ltd.

Abstract

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.

    Research areas

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

ID: 28944471