Automated classification of demographics from face images: A tutorial and validation

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Abstract

Examining disparities in social outcomes as a function of gender, age, or race has a long tradition in psychology and other social sciences. With an increasing availability of large naturalistic data sets, researchers are afforded the opportunity to study the effects of demographic characteristics with real-world data and high statistical power. However, since traditional studies rely on human raters to asses demographic characteristics, limits in participant pools can hinder researchers from analyzing large data sets. Automated procedures offer a new solution to the classification of face images. Here, we present a tutorial on how to use two face classification algorithms, Face++ and Kairos. We also test and compare their accuracy under varying conditions and provide practical recommendations for their use. Drawing on two face databases (n = 2,805 images), we find that classification accuracy is (a) relatively high, with Kairos generally outperforming Face++ (b) similar for standardized and more variable images, and (c) dependent on target demographics. For example, accuracy was lower for Hispanic and Asian (vs. Black and White) targets. In sum, we propose that automated face classification can be a useful tool for researchers interested in studying the effects of demographic characteristics in large naturalistic data sets.
Original languageEnglish
JournalSocial and Personality Psychology Compass
Publication statusAccepted/In press - 2020

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title = "Automated classification of demographics from face images: A tutorial and validation",
abstract = "Examining disparities in social outcomes as a function of gender, age, or race has a long tradition in psychology and other social sciences. With an increasing availability of large naturalistic data sets, researchers are afforded the opportunity to study the effects of demographic characteristics with real-world data and high statistical power. However, since traditional studies rely on human raters to asses demographic characteristics, limits in participant pools can hinder researchers from analyzing large data sets. Automated procedures offer a new solution to the classification of face images. Here, we present a tutorial on how to use two face classification algorithms, Face++ and Kairos. We also test and compare their accuracy under varying conditions and provide practical recommendations for their use. Drawing on two face databases (n = 2,805 images), we find that classification accuracy is (a) relatively high, with Kairos generally outperforming Face++ (b) similar for standardized and more variable images, and (c) dependent on target demographics. For example, accuracy was lower for Hispanic and Asian (vs. Black and White) targets. In sum, we propose that automated face classification can be a useful tool for researchers interested in studying the effects of demographic characteristics in large naturalistic data sets.",
author = "Bastian Jaeger and Willem Sleegers and Anthony Evans",
year = "2020",
language = "English",
journal = "Social and Personality Psychology Compass",
issn = "1751-9004",
publisher = "John Wiley and Sons Ltd",

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N2 - Examining disparities in social outcomes as a function of gender, age, or race has a long tradition in psychology and other social sciences. With an increasing availability of large naturalistic data sets, researchers are afforded the opportunity to study the effects of demographic characteristics with real-world data and high statistical power. However, since traditional studies rely on human raters to asses demographic characteristics, limits in participant pools can hinder researchers from analyzing large data sets. Automated procedures offer a new solution to the classification of face images. Here, we present a tutorial on how to use two face classification algorithms, Face++ and Kairos. We also test and compare their accuracy under varying conditions and provide practical recommendations for their use. Drawing on two face databases (n = 2,805 images), we find that classification accuracy is (a) relatively high, with Kairos generally outperforming Face++ (b) similar for standardized and more variable images, and (c) dependent on target demographics. For example, accuracy was lower for Hispanic and Asian (vs. Black and White) targets. In sum, we propose that automated face classification can be a useful tool for researchers interested in studying the effects of demographic characteristics in large naturalistic data sets.

AB - Examining disparities in social outcomes as a function of gender, age, or race has a long tradition in psychology and other social sciences. With an increasing availability of large naturalistic data sets, researchers are afforded the opportunity to study the effects of demographic characteristics with real-world data and high statistical power. However, since traditional studies rely on human raters to asses demographic characteristics, limits in participant pools can hinder researchers from analyzing large data sets. Automated procedures offer a new solution to the classification of face images. Here, we present a tutorial on how to use two face classification algorithms, Face++ and Kairos. We also test and compare their accuracy under varying conditions and provide practical recommendations for their use. Drawing on two face databases (n = 2,805 images), we find that classification accuracy is (a) relatively high, with Kairos generally outperforming Face++ (b) similar for standardized and more variable images, and (c) dependent on target demographics. For example, accuracy was lower for Hispanic and Asian (vs. Black and White) targets. In sum, we propose that automated face classification can be a useful tool for researchers interested in studying the effects of demographic characteristics in large naturalistic data sets.

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