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

Frankenstein: Learning Deep Face Representations using Small Data