ABSTRACT
Over the last 5 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 because of the availability of large annotated datasets, a better understanding of the nonlinear mapping between input images and class labels as well as the affordability of GPUs. In this chapter, we present the design details of a deep learning system for unconstrained face recognition, including face detection, face alignment, and face identification/verification modules. The quantitative performance evaluation is conducted using the IARPA JANUS Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the LFW datasets. 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) dataset. Some open issues regarding DCNNs for face identification/verification problems are then discussed.
