Multi Facet Face Construction

Hamed Alqahtani*, Manolya Kavakli-Thorne

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


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 - 5th Asian Conference, ACPR 2019, Revised Selected Papers
EditorsShivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
Number of pages9
Publication statusPublished - 23 Feb 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


Conference5th Asian Conference on Pattern Recognition, ACPR 2019
Country/TerritoryNew Zealand

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.


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


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