Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention

Authors

Makenzie L. Barr, West Virginia University
Guodong Guo, West Virginia University
Sarah E. Colby, University of Tennessee
Melissa D. Olfert, West Virginia UniversityFollow

Author ORCID Identifier

https://orcid.org/0000-0002-6332-215X

N/A

https://orcid.org/0000-0002-8298-3690

https://orcid.org/0000-0002-6686-3891

Document Type

Article

Publication Date

2018

College/Unit

Davis College of Agriculture, Natural Resources and Design

Department/Program/Center

Animal and Nutritional Sciences

Abstract

This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary.

Digital Commons Citation

Barr, Makenzie L.; Guo, Guodong; Colby, Sarah E.; and Olfert, Melissa D., "Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention" (2018). Faculty & Staff Scholarship. 1442.
https://researchrepository.wvu.edu/faculty_publications/1442

Source Citation

Barr, M., Guo, G., Colby, S., & Olfert, M. (2018). Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention. Technologies, 6(3), 83. https://doi.org/10.3390/technologies6030083

Comments

1. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).