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

Authors

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

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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Published

2017-05-03

How to Cite

Kocabey, E., Camurcu, M., Ofli, F., Aytar, Y., Marin, J., Torralba, A., & Weber, I. (2017). Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 572-575. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14923