Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media
This addresses the challenge of obtaining BMI data for obesity research without self-reporting or medical visits, though it is incremental as it applies existing computer vision methods to a new domain.
The paper tackled the problem of measuring body mass index (BMI) by developing a computer vision tool to infer BMI from social media images, releasing it to aid research on social aspects of body weight.
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.