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We study the problem of extracting biometric informa- tion 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 like- lihood analysis. We then develop a learning-based method that demonstrates this phenomenon in real settings, exploit- ing the subtle cues in the shadows that are the source of the leakage without requiring any labeled real data. In par- ticular, our approach relies on building synthetic scenes composed of 3D face models obtained from a single photo- graph 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 general- ize 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.