  Cross-domain biometrics has been emerging as a new necessity, which poses
several additional challenges, including harsh illumination changes, noise,
pose variation, among others. In this paper, we explore approaches to
cross-domain face verification, comparing self-portrait photographs ("selfies")
to ID documents. We approach the problem with proper image photometric
adjustment and data standardization techniques, along with deep learning
methods to extract the most prominent features from the data, reducing the
effects of domain shift in this problem. We validate the methods using a novel
dataset comprising 50 individuals. The obtained results are promising and
indicate that the adopted path is worth further investigation.
