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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
PublisherAAAI press
Pages572-575
Number of pages4
ISBN (Electronic)9781577357889
Publication statusPublished - 1 Jan 2017
Event11th International Conference on Web and Social Media, ICWSM 2017 - Montreal, Canada
Duration: 15 May 201718 May 2017

Other

Other11th International Conference on Web and Social Media, ICWSM 2017
CountryCanada
CityMontreal
Period15/5/1718/5/17

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ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

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. In Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017 (pp. 572-575). AAAI press.