  Over the last five years, methods based on Deep Convolutional Neural Networks
(DCNNs) have shown impressive performance improvements for object detection and
recognition problems. This has been made possible due to the availability of
large annotated datasets, a better understanding of the non-linear mapping
between input images and class labels as well as the affordability of GPUs. In
this paper, we present the design details of a deep learning system for
unconstrained face recognition, including modules for face detection,
association, alignment and face verification. The quantitative performance
evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS
Challenge Set 2 (JANUS CS2), and the LFW dataset. The IJB-A dataset includes
real-world unconstrained faces of 500 subjects with significant pose and
illumination variations which are much harder than the Labeled Faces in the
Wild (LFW) and Youtube Face (YTF) datasets. JANUS CS2 is the extended version
of IJB-A which contains not only all the images/frames of IJB-A but also
includes the original videos for evaluating the video-based face verification
system. Some open issues regarding DCNNs for face verification problems are
then discussed.
