Abstract
We propose a face recognition method using distinctive triangle encoding and nonlocal constraint-based sparse representation (TENCSR). With TENCSR, first the pixel values of all images are used as the low-level features. Next, a clustering method is proposed by considering the density distribution of target data, named shared weights support vector data description (SW-SVDD), which makes the obtained decision boundary closer to the optimal. On the basis of SW-SVDD, a more distinctive triangle encoding (MDTE) method is introduced by considering the cluster center information and the size information of cluster, which makes the encoded features more distinctive. Then the high-level features are obtained by encoding those low-level features using MDTE. Meanwhile, a nonlocal constraint-based sparse representation classifier (NC-SRC) is proposed by the biological discovery that dissimilar inputs have dissimilar codes. Finally, those high-level features are classified by the proposed NC-SRC. Experimental results on Georgia Tech, CVL, IMM, FRGC, and AR databases show that our TENCSR outperforms some state-of-the-art algorithms.
Published Version
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