CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2009 vol.31 Issue No.02 - February

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Issue No.02 - February (2009 vol.31)

pp: 210-227

John Wright , University of Illinois at Urbana-Champaign, Urbana

Allen Y. Yang , University of California, Berkeley, Berkeley

Arvind Ganesh , University of Illinois at Urbana-Champaign, Urbana

S. Shankar Sastry , UC Berkley UC Berkley, Berkeley

Yi Ma , University of Illinois at Urbana-Champaign, Urbana

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.79

ABSTRACT

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 \ell^{1}-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.

INDEX TERMS

Face recognition, feature extraction, occlusion and corruption, sparse representation, compressed sensing, \ell^{1}--minimization, validation and outlier rejection.

CITATION

John Wright, Allen Y. Yang, Arvind Ganesh, S. Shankar Sastry, Yi Ma, "Robust Face Recognition via Sparse Representation",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.31, no. 2, pp. 210-227, February 2009, doi:10.1109/TPAMI.2008.79REFERENCES

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