Multi facet face construction

Hamed Alqahtani*, Manolya Kavakli-Thorne

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

To generate a multi-faceted view, from a single image has always been a challenging problem for decades. Recent developments in technology enable us to tackle this problem effectively. Previously, Several Generative Adversarial Network (GAN) based models have been used to deal with this problem as linear GAN, linear framework, a generator (generally encoder-decoder), followed by the discriminator. Such structures helped to some extent, but are not powerful enough to tackle this problem effectively. 

In this paper, we propose a GAN based dual-architecture model called DUO-GAN. In the proposed model, we add a second pathway in addition to the linear framework of GAN with the aim of better learning of the embedding space. In this model, we propose two learning paths, which compete with each other in a parameter-sharing manner. Furthermore, the proposed two-pathway framework primarily trains multiple sub-models, which combine to give realistic results. The experimental results of DUO-GAN outperform state of the art models in the field.

Original languageEnglish
Title of host publicationPattern Recognition
Subtitle of host publication5th Asian Conference, ACPR 2019 Auckland, New Zealand, November 26–29, 2019 Revised Selected Papers, Part II
EditorsShivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages362-370
Number of pages9
ISBN (Electronic)9783030412999
ISBN (Print)9783030412982
DOIs
Publication statusPublished - 2020
Event5th Asian Conference on Pattern Recognition, ACPR 2019 - Auckland, New Zealand
Duration: 26 Nov 201929 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12047 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Asian Conference on Pattern Recognition, ACPR 2019
CountryNew Zealand
CityAuckland
Period26/11/1929/11/19

Keywords

  • GAN
  • Machine learning
  • Multi-faceted face construction
  • Neural network

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