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

## TL;DR

This paper presents a computer vision method to estimate individuals' BMI from social media images, aiming to facilitate social and health research without direct measurements.

## Contribution

It introduces a novel approach for inferring BMI from images and releases a tool to support social and health studies related to body weight.

## Key findings

- Demonstrates feasibility of BMI inference from social media images
- Provides a publicly available tool for BMI estimation
- Supports research on social aspects of body weight

## 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.