  Occlusion in face recognition is a common yet challenging problem. While
sparse representation based classification (SRC) has been shown promising
performance in laboratory conditions (i.e. noiseless or random pixel
corrupted), it performs much worse in practical scenarios. In this paper, we
consider the practical face recognition problem, where the occlusions are
predictable and available for sampling. We propose the structured occlusion
coding (SOC) to address occlusion problems. The structured coding here lies in
two folds. On one hand, we employ a structured dictionary for recognition. On
the other hand, we propose to use the structured sparsity in this formulation.
Specifically, SOC simultaneously separates the occlusion and classifies the
image. In this way, the problem of recognizing an occluded image is turned into
seeking a structured sparse solution on occlusion-appended dictionary. In order
to construct a well-performing occlusion dictionary, we propose an occlusion
mask estimating technique via locality constrained dictionary (LCD), showing
striking improvement in occlusion sample. On a category-specific occlusion
dictionary, we replace norm sparsity with the structured sparsity which is
shown more robust, further enhancing the robustness of our approach. Moreover,
SOC achieves significant improvement in handling large occlusion in real world.
Extensive experiments are conducted on public data sets to validate the
superiority of the proposed algorithm.
