AbstractBody 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 wo
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