  The performance of modern face recognition systems is a function of the
dataset on which they are trained. Most datasets are largely biased toward
"near-frontal" views with benign lighting conditions, negatively effecting
recognition performance on images that do not meet these criteria. The proposed
approach demonstrates how a baseline training set can be augmented to increase
pose and lighting variability using semi-synthetic images with simulated pose
and lighting conditions. The semi-synthetic images are generated using a fast
and robust 3-d shape estimation and rendering pipeline which includes the full
head and background. Various methods of incorporating the semi-synthetic
renderings into the training procedure of a state of the art deep neural
network-based recognition system without modifying the structure of the network
itself are investigated. Quantitative results are presented on the challenging
IJB-A identification dataset using a state of the art recognition pipeline as a
baseline.
