  Recently the sparse representation based classification (SRC) has been
proposed for robust face recognition (FR). In SRC, the testing image is coded
as a sparse linear combination of the training samples, and the representation
fidelity is measured by the l2-norm or l1-norm of the coding residual. Such a
sparse coding model assumes that the coding residual follows Gaussian or
Laplacian distribution, which may not be effective enough to describe the
coding residual in practical FR systems. Meanwhile, the sparsity constraint on
the coding coefficients makes SRC's computational cost very high. In this
paper, we propose a new face coding model, namely regularized robust coding
(RRC), which could robustly regress a given signal with regularized regression
coefficients. By assuming that the coding residual and the coding coefficient
are respectively independent and identically distributed, the RRC seeks for a
maximum a posterior solution of the coding problem. An iteratively reweighted
regularized robust coding (IR3C) algorithm is proposed to solve the RRC model
efficiently. Extensive experiments on representative face databases demonstrate
that the RRC is much more effective and efficient than state-of-the-art sparse
representation based methods in dealing with face occlusion, corruption,
lighting and expression changes, etc.
