Robust Face Recognition via Block Sparse Bayesian Learning

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 is found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this paper 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 and the AR face database show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.