Face recognition via Weighted Sparse Representation

Face recognition using Sparse Representation based Classification (SRC) is a new hot technique in recent years. SRC can be regarded as a generalization of Nearest Neighbor and Nearest Feature Subspace. This paper first reviews the Nearest Feature Classifiers (NFCs), including Nearest Neighbor (NN), Nearest Feature Line (NFL), Nearest Feature Plane (NFP) and Nearest Feature Subspace (NFS), and formulates them as general optimization problems, which provides a new perspective for understanding NFCs and SRC. Then a locality Weighted Sparse Representation based Classification (WSRC) method is proposed. WSRC utilizes both data locality and linearity; it can be regarded as extensions of SRC, but the coding is local. Experimental results on the Extended Yale B, AR databases and several data sets from the UCI repository show that WSRC is more effective than SRC.

► Formulate the Nearest Feature Classifiers as general optimization problems. ► Propose a Weighted Sparse Representation based Classification (WSRC) method. ► WSRC is an extension of SRC. ► Experimental results on face databases show the superior of WSRC.