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Technologies 2018, 6(3), 83; https://doi.org/10.3390/technologies6030083

Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention

1
Davis College of Agriculture, Natural Resources and Design, Division of Animal Nutrition and Science, Human Nutrition and Foods, West Virginia University, Morgantown, WV 26506, USA
2
Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV 26506, USA
3
Department of Nutrition, The University of Tennessee, Knoxville, TN 37996-1920, USA
*
Author to whom correspondence should be addressed.
Received: 31 July 2018 / Revised: 24 August 2018 / Accepted: 26 August 2018 / Published: 31 August 2018
(This article belongs to the Section Information and Communication Technologies)
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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. View Full-Text
Keywords: Body Mass Index (BMI); facial image; BMI prediction; young adults Body Mass Index (BMI); facial image; BMI prediction; young adults
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Barr, M.L.; Guo, G.; Colby, S.E.; Olfert, M.D. Detecting Body Mass Index from a Facial Photograph in Lifestyle Intervention. Technologies 2018, 6, 83.

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