Testing the utility of a data-driven approach for assessing BMI from face images

Karin Wolffhechel, Amanda C. Hahn, Hanne Jarmer, Claire I. Fisher, Benedict C. Jones, Lisa M. Debruine

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere0140347
Number of pages10
JournalPLoS ONE
Volume10
Issue number10
DOIs
Publication statusPublished - 13 Oct 2015

Keywords

  • attractiveness ratings
  • human experiment
  • two dimensional imaging
  • theoretical model
  • body mass index

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