Cargando…

Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images

Several lines of evidence suggest that facial cues of adiposity may be important for human social interaction. However, tests for quantifiable cues of body mass index (BMI) in the face have examined only a small number of facial proportions and these proportions were found to have relatively low pre...

Descripción completa

Detalles Bibliográficos
Autores principales: Wolffhechel, Karin, Hahn, Amanda C., Jarmer, Hanne, Fisher, Claire I., Jones, Benedict C., DeBruine, Lisa M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603950/
https://www.ncbi.nlm.nih.gov/pubmed/26460526
http://dx.doi.org/10.1371/journal.pone.0140347
_version_ 1782394987595431936
author Wolffhechel, Karin
Hahn, Amanda C.
Jarmer, Hanne
Fisher, Claire I.
Jones, Benedict C.
DeBruine, Lisa M.
author_facet Wolffhechel, Karin
Hahn, Amanda C.
Jarmer, Hanne
Fisher, Claire I.
Jones, Benedict C.
DeBruine, Lisa M.
author_sort Wolffhechel, Karin
collection PubMed
description Several lines of evidence suggest that facial cues of adiposity may be important for human social interaction. However, tests for quantifiable cues of body mass index (BMI) in the face have examined only a small number of facial proportions and these proportions were found to have relatively low predictive power. Here we employed a data-driven approach in which statistical models were built using principal components (PCs) derived from objectively defined shape and color characteristics in face images. The predictive power of these models was then compared with models based on previously studied facial proportions (perimeter-to-area ratio, width-to-height ratio, and cheek-to-jaw width). Models based on 2D shape-only PCs, color-only PCs, and 2D shape and color PCs combined each performed significantly and substantially better than models based on one or more of the previously studied facial proportions. A non-linear PC model considering both 2D shape and color PCs was the best predictor of BMI. These results highlight the utility of a “bottom-up”, data-driven approach for assessing BMI from face images.
format Online
Article
Text
id pubmed-4603950
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-46039502015-10-20 Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images Wolffhechel, Karin Hahn, Amanda C. Jarmer, Hanne Fisher, Claire I. Jones, Benedict C. DeBruine, Lisa M. PLoS One Research Article Several lines of evidence suggest that facial cues of adiposity may be important for human social interaction. However, tests for quantifiable cues of body mass index (BMI) in the face have examined only a small number of facial proportions and these proportions were found to have relatively low predictive power. Here we employed a data-driven approach in which statistical models were built using principal components (PCs) derived from objectively defined shape and color characteristics in face images. The predictive power of these models was then compared with models based on previously studied facial proportions (perimeter-to-area ratio, width-to-height ratio, and cheek-to-jaw width). Models based on 2D shape-only PCs, color-only PCs, and 2D shape and color PCs combined each performed significantly and substantially better than models based on one or more of the previously studied facial proportions. A non-linear PC model considering both 2D shape and color PCs was the best predictor of BMI. These results highlight the utility of a “bottom-up”, data-driven approach for assessing BMI from face images. Public Library of Science 2015-10-13 /pmc/articles/PMC4603950/ /pubmed/26460526 http://dx.doi.org/10.1371/journal.pone.0140347 Text en © 2015 Wolffhechel et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Wolffhechel, Karin
Hahn, Amanda C.
Jarmer, Hanne
Fisher, Claire I.
Jones, Benedict C.
DeBruine, Lisa M.
Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images
title Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images
title_full Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images
title_fullStr Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images
title_full_unstemmed Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images
title_short Testing the Utility of a Data-Driven Approach for Assessing BMI from Face Images
title_sort testing the utility of a data-driven approach for assessing bmi from face images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603950/
https://www.ncbi.nlm.nih.gov/pubmed/26460526
http://dx.doi.org/10.1371/journal.pone.0140347
work_keys_str_mv AT wolffhechelkarin testingtheutilityofadatadrivenapproachforassessingbmifromfaceimages
AT hahnamandac testingtheutilityofadatadrivenapproachforassessingbmifromfaceimages
AT jarmerhanne testingtheutilityofadatadrivenapproachforassessingbmifromfaceimages
AT fisherclairei testingtheutilityofadatadrivenapproachforassessingbmifromfaceimages
AT jonesbenedictc testingtheutilityofadatadrivenapproachforassessingbmifromfaceimages
AT debruinelisam testingtheutilityofadatadrivenapproachforassessingbmifromfaceimages