We study the problem of extracting biometric information of individuals by
looking at shadows of objects cast on diffuse surfaces. We show that the
biometric information leakage from shadows can be sufficient for reliable
identity inference under representative scenarios via a maximum likelihood
analysis. We then develop a learning-based method that demonstrates this
phenomenon in real settings, exploiting the subtle cues in the shadows that are
the source of the leakage without requiring any labeled real data. In
particular, our approach relies on building synthetic scenes composed of 3D
face models obtained from a single photograph of each identity. We transfer
what we learn from the synthetic data to the real data using domain adaptation
in a completely unsupervised way. Our model is able to generalize well to the
real domain and is robust to several variations in the scenes. We report high
classification accuracies in an identity classification task that takes place
in a scene with unknown geometry and occluding objects