Multi Facet Face Construction

Hamed Alqahtani; Manolya Kavakli-Thorne

doi:10.1007/978-3-030-41299-9_28

Multi Facet Face Construction

Hamed Alqahtani^*, Manolya Kavakli-Thorne

^*Corresponding author for this work

Research output: Chapter in Book/Published conference output › Conference publication

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 language	English
Title of host publication	Pattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers
Editors	Shivakumara Palaiahnakote, Gabriella Sanniti di Baja, Liang Wang, Wei Qi Yan
Pages	362-370
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-030-41299-9_28
Publication status	Published - 23 Feb 2020
Event	5th Asian Conference on Pattern Recognition, ACPR 2019 - Auckland, New Zealand Duration: 26 Nov 2019 → 29 Nov 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12047 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th Asian Conference on Pattern Recognition, ACPR 2019
Country/Territory	New Zealand
City	Auckland
Period	26/11/19 → 29/11/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

GAN
Machine learning
Multi-faceted face construction
Neural network

Access to Document

10.1007/978-3-030-41299-9_28

Cite this

Alqahtani, H., & Kavakli-Thorne, M. (2020). Multi Facet Face Construction. In S. Palaiahnakote, G. Sanniti di Baja, L. Wang, & W. Q. Yan (Eds.), Pattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers (pp. 362-370). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12047 LNCS). https://doi.org/10.1007/978-3-030-41299-9_28

Alqahtani, Hamed ; Kavakli-Thorne, Manolya. / Multi Facet Face Construction. Pattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers. editor / Shivakumara Palaiahnakote ; Gabriella Sanniti di Baja ; Liang Wang ; Wei Qi Yan. 2020. pp. 362-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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author = "Hamed Alqahtani and Manolya Kavakli-Thorne",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 5th Asian Conference on Pattern Recognition, ACPR 2019 ; Conference date: 26-11-2019 Through 29-11-2019",

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Alqahtani, H & Kavakli-Thorne, M 2020, Multi Facet Face Construction. in S Palaiahnakote, G Sanniti di Baja, L Wang & WQ Yan (eds), Pattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12047 LNCS, pp. 362-370, 5th Asian Conference on Pattern Recognition, ACPR 2019, Auckland, New Zealand, 26/11/19. https://doi.org/10.1007/978-3-030-41299-9_28

Multi Facet Face Construction. / Alqahtani, Hamed; Kavakli-Thorne, Manolya.
Pattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers. ed. / Shivakumara Palaiahnakote; Gabriella Sanniti di Baja; Liang Wang; Wei Qi Yan. 2020. p. 362-370 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12047 LNCS).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Multi Facet Face Construction

AU - Alqahtani, Hamed

AU - Kavakli-Thorne, Manolya

PY - 2020/2/23

Y1 - 2020/2/23

N2 - 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.

AB - 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.

KW - GAN

KW - Machine learning

KW - Multi-faceted face construction

KW - Neural network

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SN - 9783030412982

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Palaiahnakote, Shivakumara

A2 - Sanniti di Baja, Gabriella

A2 - Wang, Liang

A2 - Yan, Wei Qi

T2 - 5th Asian Conference on Pattern Recognition, ACPR 2019

Y2 - 26 November 2019 through 29 November 2019

ER -

Alqahtani H, Kavakli-Thorne M. Multi Facet Face Construction. In Palaiahnakote S, Sanniti di Baja G, Wang L, Yan WQ, editors, Pattern Recognition - 5th Asian Conference, ACPR 2019, Revised Selected Papers. 2020. p. 362-370. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-41299-9_28

Multi Facet Face Construction

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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