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

Kocabey, Enes (Massachusetts Institute of Technology) | Camurcu, Mustafa (Northeastern University) | Ofli, Ferda (Hamad Bin Khalifa University) | Aytar, Yusuf (Massachusetts Institute of Technology) | Marin, Javier (Massachusetts Institute of Technology) | Torralba, Antonio (Massachusetts Institute of Technology) | Weber, Ingmar (Hamad Bin Khalifa University)

AAAI Conferences 

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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