Face-to-BMI: Using computer vision to infer body mass index on social media

Enes Kocabey; Mustafa Camurcu; Ferda Ofli; Yusuf Aytar; Javier Marin; Antonio Torralba; Ingmar Weber

Face-to-BMI: Using computer vision to infer body mass index on social media

Enes Kocabey, Mustafa Camurcu, Ferda Ofli, Yusuf Aytar, Javier Marin, Antonio Torralba, Ingmar Weber

Qatar Computing Research Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

34 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
Publisher	AAAI Press
Pages	572-575
Number of pages	4
ISBN (Electronic)	9781577357889
Publication status	Published - 2017
Event	11th International Conference on Web and Social Media, ICWSM 2017 - Montreal, Canada Duration: 15 May 2017 → 18 May 2017

Publication series

Name	Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017

Conference

Conference	11th International Conference on Web and Social Media, ICWSM 2017
Country/Territory	Canada
City	Montreal
Period	15/05/17 → 18/05/17

Cite this

Kocabey, E., Camurcu, M., Ofli, F., Aytar, Y., Marin, J., Torralba, A., & Weber, I. (2017). Face-to-BMI: Using computer vision to infer body mass index on social media. In Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017 (pp. 572-575). (Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017). AAAI Press.

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title = "Face-to-BMI: Using computer vision to infer body mass index on social media",

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.",

author = "Enes Kocabey and Mustafa Camurcu and Ferda Ofli and Yusuf Aytar and Javier Marin and Antonio Torralba and Ingmar Weber",

note = "Publisher Copyright: {\textcopyright} Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 11th International Conference on Web and Social Media, ICWSM 2017 ; Conference date: 15-05-2017 Through 18-05-2017",

year = "2017",

language = "English",

series = "Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017",

publisher = "AAAI Press",

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Kocabey, E, Camurcu, M, Ofli, F, Aytar, Y, Marin, J, Torralba, A & Weber, I 2017, Face-to-BMI: Using computer vision to infer body mass index on social media. in Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017. Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017, AAAI Press, pp. 572-575, 11th International Conference on Web and Social Media, ICWSM 2017, Montreal, Canada, 15/05/17.

Face-to-BMI: Using computer vision to infer body mass index on social media. / Kocabey, Enes; Camurcu, Mustafa; Ofli, Ferda et al.
Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017. AAAI Press, 2017. p. 572-575 (Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Face-to-BMI

T2 - 11th International Conference on Web and Social Media, ICWSM 2017

AU - Kocabey, Enes

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AU - Marin, Javier

AU - Torralba, Antonio

AU - Weber, Ingmar

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AB - 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.

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M3 - Conference contribution

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T3 - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017

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BT - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017

PB - AAAI Press

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ER -

Face-to-BMI: Using computer vision to infer body mass index on social media

Abstract

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