Most contemporary face recognition algorithms work well under laboratory conditions but degrade when tested in less-controlled environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, alignment, pose, and occlusion. In this paper, we propose a simple and practical face recognition system that achieves a high degree of robustness and stability to all these variations. We demonstrate how to use tools from sparse representation to align a test face image with a set of frontal training images in the presence of significant registration error and occlusion. We thoroughly characterize the region of attraction for our alignment algorithm on public face datasets such as Multi-PIE. We further study how to obtain a sufficient set of training illuminations for linearly interpolating practical lighting conditions. We have implemented a complete face recognition system, including a projector-based training acquisition system, in order to evaluate how our algorithms work under practical testing conditions. We show that our system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the proposed illuminations as training.