Robust Face Recognition via Sparse Representation
We consider the problem of automatically recognizing human faces from frontal views with varying expression and
illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression
models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse
representation computed by
-minimization, we propose a general classification algorithm for (image-based) object recognition. This
new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For
feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical.
What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly
computed. Unconventional features such as downsampled images and random projections perform just as well as conventional
features such as Eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted
by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the
fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how
much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We
conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the
above claims.
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