  Numerous activities in our daily life require us to verify who we are by
showing our ID documents containing face images, such as passports and driver
licenses, to human operators. However, this process is slow, labor intensive
and unreliable. As such, an automated system for matching ID document photos to
live face images (selfies) in real time and with high accuracy is required. In
this paper, we propose DocFace+ to meet this objective. We first show that
gradient-based optimization methods converge slowly (due to the underfitting of
classifier weights) when many classes have very few samples, a characteristic
of existing ID-selfie datasets. To overcome this shortcoming, we propose a
method, called dynamic weight imprinting (DWI), to update the classifier
weights, which allows faster convergence and more generalizable
representations. Next, a pair of sibling networks with partially shared
parameters are trained to learn a unified face representation with
domain-specific parameters. Cross-validation on an ID-selfie dataset shows that
while a publicly available general face matcher (SphereFace) only achieves a
True Accept Rate (TAR) of 59.29+-1.55% at a False Accept Rate (FAR) of 0.1% on
the problem, DocFace+ improves the TAR to 97.51+-0.40%.
