Robust face verification via Bayesian sparse representation
This research proposes a novel Bayesian sparse representation (BSR) method along with extracting facial parameters of SIFT to create sparse dictionaries, which are invariant to rotation, scale, and shift. By using K-means and information theory, a new dictionary called extended dictionary is developed. Compared with conventional orthogonal matching pursuit (OMP) algorithm, the proposed system that utilized Bayesian method to model the optimization problem of sparse representation can reduce the uncertainty of observed signals and expand the modeling ability of dictionaries by using variance. The experimental results show that the proposed extended dictionary can enhance the sparsity. Furthermore, it can improve accuracy rates of face identification and residues of reconstruction.