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


  • 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




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.




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. https://doi.org/10.1609/icwsm.v11i1.14923