Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media

Enes Kocabey; Mustafa Camurcu; Ferda Ofli; Yusuf Aytar; Javier Marin; Antonio Torralba; Ingmar Weber

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arXiv · 1703.03156

Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media

Enes Kocabey·Mustafa Camurcu·Ferda Ofli·Yusuf Aytar·Javier Marin·Antonio Torralba·Ingmar Weber

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Abstract

A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, "fat shaming" and other forms of "sizeism" are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.

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Enes Kocabey, Mustafa Camurcu, Ferda Ofli, Yusuf Aytar, Javier Marin, Antonio Torralba, Ingmar Weber. 2017-03-09. Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media. https://arxiv.org/abs/1703.03156

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