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

Authors

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

Massachusetts Institute of Technology,Northeastern University,Hamad Bin Khalifa University,Massachusetts Institute of Technology,Massachusetts Institute of Technology,Massachusetts Institute of Technology,Hamad Bin Khalifa University


Proceedings:

Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media

Volume

Issue:

Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media

Track:

Poster Papers

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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.

DOI:

10.1609/icwsm.v11i1.14923


ICWSM

Vol. 11 No. 1 (2017): Eleventh International AAAI Conference on Web and Social Media