A generative model of worldwide facial appearance

Zachary Bessinger; Nathan Jacobs

doi:10.1109/WACV.2019.00172

A generative model of worldwide facial appearance

Zachary Bessinger, Nathan Jacobs

Computer Science

Producción científica: Conference contribution › revisión exhaustiva

5 Citas (Scopus)

Resumen

Human appearance depends on many proximate factors, including age, gender, ethnicity, and personal style choices. In this work, we model the relationship between human appearance and geographic location, which can impact these factors in complex ways. We propose GPS2Face, a dual-component generative network architecture that enables flexible facial generation with fine-grained control of latent factors. We use facial landmarks as a guide to synthesize likely faces for locations around in the world. We train our model on a large-scale dataset of geotagged faces and evaluate our proposed model, both qualitatively and quantitatively, against previous work.

Idioma original	English
Título de la publicación alojada	Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Páginas	1569-1578
Número de páginas	10
ISBN (versión digital)	9781728119755
DOI	https://doi.org/10.1109/WACV.2019.00172
Estado	Published - mar 4 2019
Evento	19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 - Waikoloa Village, United States Duración: ene 7 2019 → ene 11 2019

Serie de la publicación

Nombre	Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019

Conference

Conference	19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
País/Territorio	United States
Ciudad	Waikoloa Village
Período	1/7/19 → 1/11/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE

Financiación

We gratefully acknowledge the financial support of NSF CAREER grant IIS-1553116 and computing resources provided by the Univ. of Kentucky Center for Computational Sciences, including a Power8 system donated by IBM.

Financiadores	Número del financiador
Univ. of Kentucky Center for Computational Sciences
National Science Foundation Arctic Social Science Program	IIS-1553116
National Science Foundation Arctic Social Science Program
International Business Machines Corporation

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Computer Science Applications

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10.1109/WACV.2019.00172

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@inproceedings{b08250795d0f4b6489f21f4919cd58e5,

title = "A generative model of worldwide facial appearance",

abstract = "Human appearance depends on many proximate factors, including age, gender, ethnicity, and personal style choices. In this work, we model the relationship between human appearance and geographic location, which can impact these factors in complex ways. We propose GPS2Face, a dual-component generative network architecture that enables flexible facial generation with fine-grained control of latent factors. We use facial landmarks as a guide to synthesize likely faces for locations around in the world. We train our model on a large-scale dataset of geotagged faces and evaluate our proposed model, both qualitatively and quantitatively, against previous work.",

author = "Zachary Bessinger and Nathan Jacobs",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE; 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 ; Conference date: 07-01-2019 Through 11-01-2019",

year = "2019",

month = mar,

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doi = "10.1109/WACV.2019.00172",

language = "English",

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booktitle = "Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019",

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Bessinger, Z & Jacobs, N 2019, A generative model of worldwide facial appearance. En Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019., 8658761, Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019, pp. 1569-1578, 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019, Waikoloa Village, United States, 1/7/19. https://doi.org/10.1109/WACV.2019.00172

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AB - Human appearance depends on many proximate factors, including age, gender, ethnicity, and personal style choices. In this work, we model the relationship between human appearance and geographic location, which can impact these factors in complex ways. We propose GPS2Face, a dual-component generative network architecture that enables flexible facial generation with fine-grained control of latent factors. We use facial landmarks as a guide to synthesize likely faces for locations around in the world. We train our model on a large-scale dataset of geotagged faces and evaluate our proposed model, both qualitatively and quantitatively, against previous work.

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A generative model of worldwide facial appearance

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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