  Numerous activities in our daily life, including transactions, access to
services and transportation, require us to verify who we are by showing our ID
documents containing face images, e.g. passports and driver licenses. An
automatic system for matching ID document photos to live face images in real
time with high accuracy would speedup the verification process and remove the
burden on human operators. In this paper, by employing the transfer learning
technique, we propose a new method, DocFace, to train a domain-specific network
for ID document photo matching without a large dataset. Compared with the
baseline of applying existing methods for general face recognition to this
problem, our method achieves considerable improvement. A cross validation on an
ID-Selfie dataset shows that DocFace improves the TAR from 61.14% to 92.77% at
FAR=0.1%. Experimental results also indicate that given more training data, a
viable system for automatic ID document photo matching can be developed and
deployed.
