Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they
are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image
misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled, and test images
are only loosely controlled. We propose a conceptually simple face recognition system that achieves a high degree of robustness and
stability to illumination variation, image misalignment, and partial occlusion. The system uses tools from sparse representation to align
a test face image to a set of frontal training images. The region of attraction of our alignment algorithm is computed empirically for
public face datasets such as Multi-PIE. We demonstrate how to capture a set of training images with enough illumination variation
that they span test images taken under uncontrolled illumination. In order to evaluate how our algorithms work under practical testing
conditions, we have implemented a complete face recognition system, including a projector-based training acquisition system. Our
system can efficiently and effectively recognize faces under a variety of realistic conditions, using only frontal images under the
proposed illuminations as training.
