Locality-sensitive kernel sparse representation classification for face recognition

• A new classification method called LS-KSRC is proposed.
• LS-KSRC integrates both sparsity and data locality in the kernel feature space.
• LS-KSRC’s closed form solution of the l1-norm minimization problem is presented.
• LS-KSRC outperforms KSRC, SRC, LLC, SVM, NN, and NS.

In this paper a new classification method called locality-sensitive kernel sparse representation classification (LS-KSRC) is proposed for face recognition. LS-KSRC integrates both sparsity and data locality in the kernel feature space rather than in the original feature space. LS-KSRC can learn more discriminating sparse representation coefficients for face recognition. The closed form solution of the l1-norm minimization problem for LS-KSRC is also presented. LS-KSRC is compared with kernel sparse representation classification (KSRC), sparse representation classification (SRC), locality-constrained linear coding (LLC), support vector machines (SVM), the nearest neighbor (NN), and the nearest subspace (NS). Experimental results on three benchmarking face databases, i.e., the ORL database, the Extended Yale B database, and the CMU PIE database, demonstrate the promising performance of the proposed method for face recognition, outperforming the other used methods.