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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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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 |
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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 |
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