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

By Enes Kocabey, Mustafa Çamurcu, Ferda Ofli, Yusuf Aytar, Javier Marín, Antonio Torralba, Ingmar Weber. 2017

78 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Pharmacy

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Citations in the corpus

What Does Explainable AI Really Mean? A New Conceptualization of Perspectives (2017, background): “…(AI) systems that make decisions on possibly unethical grounds, e.g. when they predict a person’s weight and health by their social media images [8] or the world region they are from [7] as part of a downstream determination about their future, like when they will quit their job [12], commit a…”
Can you tell where in India I am from? Comparing humans and computers on fine-grained race face classification (2017, result): “Overall we found that CNN models did well in general and VGG-Face in particular was most informative amongst CNN based models, the informativeness of VGG-Face features for face attribute categorisation has been reported in other independent studies such as well[10][5].”
Designing a Moral Compass for the Future of Computer Vision Using Speculative Analysis (2017, unknown): “The potential for privacy issues appears pervasive given work currently emerging in CV research such as inferring health metrics from social media images [35] or de-anonymizing blurred images [45].”