We propose a scalable solution to the problem of real-world face recognition when both training and test faces are under varying pose and illumination. Our proposed classifier solves a sparse approximation problem in a learned transform domain. Our algorithm uses a cascaded solution to significantly reduce the computational cost of the classification process. The cascaded solution first applies a more efficient Subspace Pursuit Algorithm on the test image, and only runs a more accurate ?_1 -minimization algorithm on those face images for which the Subspace Pursuit does not have enough confidence in prediction. We also show the application of our algorithm in automatic face annotation of media objects, and show that on average our algorithm achieves about 94% annotation accuracy over the celebrity benchmark dataset.
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