  We propose a new deep network structure for unconstrained face recognition.
The proposed network integrates several key components together in order to
characterize complex data distributions, such as in unconstrained face images.
Inspired by recent progress in deep networks, we consider some important
concepts, including multi-scale feature learning, dense connections of network
layers, and weighting different network flows, for building our deep network
structure. The developed network is evaluated in unconstrained face matching,
showing the capability of learning complex data distributions caused by face
images with various qualities.
