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The performance of modern face recognition systems is a function of thedataset on which they are trained. Most datasets are largely biased toward"near-frontal" views with benign lighting conditions, negatively effectingrecognition performance on images that do not meet these criteria. The proposedapproach demonstrates how a baseline training set can be augmented to increasepose and lighting variability using semi-synthetic images with simulated poseand lighting conditions. The semi-synthetic images are generated using a fastand robust 3-d shape estimation and rendering pipeline which includes the fullhead and background. Various methods of incorporating the semi-syntheticrenderings into the training procedure of a state of the art deep neuralnetwork-based recognition system without modifying the structure of the networkitself are investigated. Quantitative results are presented on the challengingIJB-A identification dataset using a state of the art recognition pipeline as abaseline.

Dataset Augmentation for Pose and Lighting Invariant Face Recognition