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.

中文翻译：

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从面部图像自动进行人口统计分类：教程和验证

根据性别，年龄或种族来检查社会结果的差异在心理学和其他社会科学中有着悠久的传统。随着大型自然数据集可用性的提高，研究人员将有机会利用现实世界的数据和高统计能力来研究人口统计学特征的影响。但是，由于传统研究依赖于人类评估者来评估人口统计特征，因此参与者人数的限制可能会阻碍研究人员分析大型数据集。自动化程序为面部图像的分类提供了新的解决方案。在这里，我们提供了有关如何使用两种人脸分类算法Face ++和Kairos的教程。我们还将测试和比较它们在不同条件下的准确性，并为它们的使用提供实用的建议。在两个面部数据库上绘制（n = 2，805张图片），我们发现分类精度较高（a），Kairos的表现通常优于Face ++（b）标准化和可变图像的相似性，以及（c）取决于目标人群。例如，针对西班牙裔和亚洲（相对于黑白）目标的准确性较低。总而言之，我们提出自动面部分类可以成为有兴趣研究大型自然数据集中人口统计学特征影响的研究人员的有用工具。