  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.
