  Face recognition (FR) is an important task in pattern recognition and
computer vision. Sparse representation (SR) has been demonstrated to be a
powerful framework for FR. In general, an SR algorithm treats each face in a
training dataset as a basis function, and tries to find a sparse representation
of a test face under these basis functions. The sparse representation
coefficients then provide a recognition hint. Early SR algorithms are based on
a basic sparse model. Recently, it has been found that algorithms based on a
block sparse model can achieve better recognition rates. Based on this model,
in this study we use block sparse Bayesian learning (BSBL) to find a sparse
representation of a test face for recognition. BSBL is a recently proposed
framework, which has many advantages over existing block-sparse-model based
algorithms. Experimental results on the Extended Yale B, the AR and the CMU PIE
face databases show that using BSBL can achieve better recognition rates and
higher robustness than state-of-the-art algorithms in most cases.
