Face-to-BMI: Using computer vision to infer body mass index on social media
Author(s)Kocabey, Enes; Camurcu, Mustafa; Offli, Ferda; Aytar, Yusuf; Marin, Javier; Torralba, Antonio; Weber, Ingmar; ... Show more Show less
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the Eleventh International Conference on Web and Social Media
Association for the Advancement of Artificial Intelligence Press
Kocabey, Enes et al. "Face-to-BMI: Using computer vision to infer body mass index on social media." Proceedings of the Eleventh International Conference on Web and Social Media, May 2017, Montreal, Quebec, Canada, Association for the Advancement of Artificial Intelligence Press, 2017 © 2017 Association for the Advancement of Artificial Intelligence Press