Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race

In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that t...

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Main Authors: JUNG, Soon-Gyu, AN, Jisun, KWAK, Haewoon, SALMINEN, Joni, JANSEN, Bernard J.
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/5338
https://ink.library.smu.edu.sg/context/sis_research/article/6342/viewcontent/17839_77973_1_PB.pdf
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spelling sg-smu-ink.sis_research-63422020-10-30T03:18:56Z Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race JUNG, Soon-Gyu AN, Jisun KWAK, Haewoon SALMINEN, Joni JANSEN, Bernard J. In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that the tools are generally proficient at determining gender, with accuracy rates greater than 90%, except for IBM Bluemix. Concerning race, only one of the four tools provides this capability, Face++, with an accuracy rate of greater than 90%, although the evaluation was performed on a high-quality dataset. Inferring age appears to be a challenging problem, as all four tools performed poorly. The findings of our quantitative evaluation are helpful for future computational social science research using these tools, as their accuracy needs to be taken into account when applied to classifying individuals on social media and other contexts. Triangulation and manual verification are suggested for researchers employing these tools. 2018-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5338 https://ink.library.smu.edu.sg/context/sis_research/article/6342/viewcontent/17839_77973_1_PB.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Computational social science Measurement study Computational Engineering Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Computational social science
Measurement study
Computational Engineering
Databases and Information Systems
spellingShingle Computational social science
Measurement study
Computational Engineering
Databases and Information Systems
JUNG, Soon-Gyu
AN, Jisun
KWAK, Haewoon
SALMINEN, Joni
JANSEN, Bernard J.
Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race
description In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that the tools are generally proficient at determining gender, with accuracy rates greater than 90%, except for IBM Bluemix. Concerning race, only one of the four tools provides this capability, Face++, with an accuracy rate of greater than 90%, although the evaluation was performed on a high-quality dataset. Inferring age appears to be a challenging problem, as all four tools performed poorly. The findings of our quantitative evaluation are helpful for future computational social science research using these tools, as their accuracy needs to be taken into account when applied to classifying individuals on social media and other contexts. Triangulation and manual verification are suggested for researchers employing these tools.
format text
author JUNG, Soon-Gyu
AN, Jisun
KWAK, Haewoon
SALMINEN, Joni
JANSEN, Bernard J.
author_facet JUNG, Soon-Gyu
AN, Jisun
KWAK, Haewoon
SALMINEN, Joni
JANSEN, Bernard J.
author_sort JUNG, Soon-Gyu
title Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race
title_short Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race
title_full Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race
title_fullStr Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race
title_full_unstemmed Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race
title_sort assessing the accuracy of four popular face recognition tools for inferring gender, age, and race
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/5338
https://ink.library.smu.edu.sg/context/sis_research/article/6342/viewcontent/17839_77973_1_PB.pdf
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