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In this paper, the authors present a novel framework to address the confounding effects of illumination variation in face recognition. By augmenting the gallery set with realistically relit images, they enhance recognition performance in a classifier-independent way. They describe a novel method for single-image relighting. Morphable Reflectance Fields (MoRF), which does not require manual intervention and provides relighting superior to that of existing automatic methods. They test their framework through face recognition experiments using various state-of-the-art classifiers and popular benchmark datasets: CMU PIE, Multi-PIE, and MERL Dome. They demonstrate that their MoRF relighting and gallery augmentation framework achieves improvements in terms of both rank-1 recognition rates and ROC curves.
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