Body measurements, including weight and height, are key indicators of health. Being able to visually assess body measurements reliably is a step towards increased awareness of overweight and obesity and is thus important for public health. Nevertheless it is currently not well understood how accurately humans can assess weight and height from images, and when and how they fail. To bridge this gap, we start from 1,682 images of persons collected from the Web, each annotated with the true weight and height, and ask crowd workers to estimate the weight and height for each image. We conduct a faceted analysis taking into account characteristics of the images as well as the crowd workers assessing the images, revealing several novel findings: (1) Even after aggregation, the crowd’s accuracy is overall low. (2) We find strong evidence of contraction bias toward a reference value, such that the weight of light people and the height of short people are overestimated, whereas the weight of heavy people and the height of tall people are underestimated. (3) We estimate workers’ individual reference values using a Bayesian model, finding that reference values strongly correlate with workers’ own height and weight, indicating that workers are better at estimating people similar to themselves. (4) The weight of tall people is underestimated more than that of short people; yet, knowing the height decreases the weight error only mildly. (5) Accuracy is higher on images of females than of males, but female and male workers are no different in terms of accuracy. (6) Crowd workers improve over time if given feedback on previous guesses. Finally, we explore various bias correction models for improving the crowd’s accuracy, but find that this only leads to modest gains. Overall, this work provides important insights on biases in body measurement estimation as obesity-related conditions are on the rise.

身体测量，包括体重和身高，是健康的关键指标。能够以视觉方式可靠地评估人体测量结果是朝着增加对超重和肥胖的认识迈出的一步，因此对公共健康至关重要。但是，目前尚不十分清楚，人类如何能够准确地评估图像中的体重和身高，以及何时以及如何失败。为了弥合这一差距，我们从Web上收集的1,682张人的图像开始，每张图像都标有真实的体重和身高，并要求人群工作者估算每个图像的体重和身高。我们在考虑图像特征以及人群工作者评估图像的情况下进行了多方面的分析，揭示了一些新颖的发现：（1）即使经过汇总，人群的准确性总体还是很低的。（2）我们发现有充分的证据表明，收缩会偏向参考值，从而使轻者的体重和矮个子的身高被高估，而重者的体重和高个子的身高却被低估。（3）我们使用贝叶斯模型估计工人的个人参考值，发现参考值与工人自己的身高和体重密切相关，这表明工人更擅长估算与自己相似的人。（4）高个子的体重比矮个子的体重低估了很多；然而，知道身高只能轻微减轻体重误差。（5）在女性图像上的准确性高于男性，但是女性和男性工人的准确性没有差异。（6）如果对先前的猜测给予反馈，人群工人会随着时间的推移而改善。最后，我们探索了各种偏差校正模型以提高人群的准确性，但发现这样做只会带来适度的收益。总体而言，随着肥胖相关疾病的增加，这项工作提供了有关人体测量估计偏差的重要见解。