Exploring the geo-dependence of human face appearance

Mohammad T. Islam; Scott Workman; Hui Wu; Nathan Jacobs; Richard Souvenir

doi:10.1109/WACV.2014.6835989

Exploring the geo-dependence of human face appearance

Mohammad T. Islam, Scott Workman, Hui Wu, Nathan Jacobs, Richard Souvenir

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

The expected appearance of a human face depends strongly on age, ethnicity and gender. While these relationships are well-studied, our work explores the little-studied dependence of facial appearance on geographic location. To support this effort, we constructed GeoFaces, a large dataset of geotagged face images. We examine the geo-dependence of Eigenfaces and use two supervised methods for extracting geo-informative features. The first, canonical correlation analysis, is used to find location-dependent component images as well as the spatial direction of most significant face appearance change. The second, linear discriminant analysis, is used to find countries with relatively homogeneous, yet distinctive, facial appearance.

Original language	English
Title of host publication	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Pages	1042-1049
Number of pages	8
DOIs	https://doi.org/10.1109/WACV.2014.6835989
State	Published - 2014
Event	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 - Steamboat Springs, CO, United States Duration: Mar 24 2014 → Mar 26 2014

Publication series

Name	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014

Conference

Conference	2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Country/Territory	United States
City	Steamboat Springs, CO
Period	3/24/14 → 3/26/14

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/WACV.2014.6835989

Cite this

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title = "Exploring the geo-dependence of human face appearance",

abstract = "The expected appearance of a human face depends strongly on age, ethnicity and gender. While these relationships are well-studied, our work explores the little-studied dependence of facial appearance on geographic location. To support this effort, we constructed GeoFaces, a large dataset of geotagged face images. We examine the geo-dependence of Eigenfaces and use two supervised methods for extracting geo-informative features. The first, canonical correlation analysis, is used to find location-dependent component images as well as the spatial direction of most significant face appearance change. The second, linear discriminant analysis, is used to find countries with relatively homogeneous, yet distinctive, facial appearance.",

author = "Islam, {Mohammad T.} and Scott Workman and Hui Wu and Nathan Jacobs and Richard Souvenir",

year = "2014",

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language = "English",

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series = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014",

pages = "1042--1049",

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note = "2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014 ; Conference date: 24-03-2014 Through 26-03-2014",

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Islam, MT, Workman, S, Wu, H, Jacobs, N & Souvenir, R 2014, Exploring the geo-dependence of human face appearance. in 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014., 6835989, 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, pp. 1042-1049, 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014, Steamboat Springs, CO, United States, 3/24/14. https://doi.org/10.1109/WACV.2014.6835989

Exploring the geo-dependence of human face appearance. / Islam, Mohammad T.; Workman, Scott; Wu, Hui et al.
2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014. 2014. p. 1042-1049 6835989 (2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The expected appearance of a human face depends strongly on age, ethnicity and gender. While these relationships are well-studied, our work explores the little-studied dependence of facial appearance on geographic location. To support this effort, we constructed GeoFaces, a large dataset of geotagged face images. We examine the geo-dependence of Eigenfaces and use two supervised methods for extracting geo-informative features. The first, canonical correlation analysis, is used to find location-dependent component images as well as the spatial direction of most significant face appearance change. The second, linear discriminant analysis, is used to find countries with relatively homogeneous, yet distinctive, facial appearance.

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Exploring the geo-dependence of human face appearance

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