Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media (1703.03156v1)

Published 9 Mar 2017 in cs.HC, cs.CV, and cs.CY

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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Enes Kocabey (1 paper)
  2. Mustafa Camurcu (2 papers)
  3. Ferda Ofli (37 papers)
  4. Yusuf Aytar (36 papers)
  5. Javier Marin (13 papers)
  6. Antonio Torralba (178 papers)
  7. Ingmar Weber (66 papers)
Citations (72)

Summary

We haven't generated a summary for this paper yet.