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 (height) of light (short) people is overestimated, whereas that of heavy (tall) people is 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.

中文翻译：

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人体测量估计中的人为偏差

身体测量值，包括体重和身高，是健康的关键指标。能够可靠地视觉评估身体测量值是提高对超重和肥胖意识的一步，因此对公共卫生很重要。然而，目前尚不清楚人类如何准确地从图像中评估体重和身高，以及它们何时以及如何失败。为了弥补这一差距，我们从从网络收集的 1,682 张人物图像开始，每张都标有真实的体重和身高，并要求人群工作人员估计每张图片的体重和身高。我们进行了一个分面分析，考虑到图像的特征以及评估图像的人群，揭示了几个新的发现：（1）即使在聚合之后，人群的准确性总体上也很低。(2) 我们发现收缩偏向于参考值的有力证据，例如轻（矮）人的体重（身高）被高估，而重（高）人的体重（身高）被低估。(3) 我们使用贝叶斯模型估计工人的个体参考值，发现参考值与工人自身的身高和体重密切相关，表明工人更善于估计与自己相似的人。(4)高个子的体重比矮个子被低估的多；然而，知道身高只会轻微地减少体重误差。(5) 女性图像的准确率高于男性，但女性和男性工人在准确率方面没有区别。(6) 如果对之前的猜测给出反馈，人群工作人员会随着时间的推移而改进。最后，我们探索了各种偏差校正模型来提高人群的准确性，但发现这只会带来适度的收益。总体而言，随着肥胖相关疾病的增多，这项工作提供了关于身体测量估计偏差的重要见解。