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Category: Project

Description: 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 demographic characteristics are often determined by having participants rate images of targets, limits in participant pools can hinder researchers from analyzing large data sets. 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, (b) similar for standardized and more variable images, and (c) dependent on various factors. Kairos outperformed Face++ on all three demographic variables; accuracy was lower for Hispanic and Asian (vs. Black and White) targets; and both algorithms tended to overestimate the age of 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.