  Deep convolutional neural networks have recently proven extremely effective
for difficult face recognition problems in uncontrolled settings. To train such
networks, very large training sets are needed with millions of labeled images.
For some applications, such as near-infrared (NIR) face recognition, such large
training datasets are not publicly available and difficult to collect. In this
work, we propose a method to generate very large training datasets of synthetic
images by compositing real face images in a given dataset. We show that this
method enables to learn models from as few as 10,000 training images, which
perform on par with models trained from 500,000 images. Using our approach we
also obtain state-of-the-art results on the CASIA NIR-VIS2.0 heterogeneous face
recognition dataset.
