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.
A hand vein recognition method using local Gabor ordinal measure (OM) is presented. Gabor OM uses eight encoding masks to extract four types of features, which are derived from the magnitude, phase, real, and imaginary components of vein image after Gabor filtering, respectively, and then concatenates these feature histograms. Block-based pattern matching introduced with a Fisher linear discriminant adopts the “divide and conquer” strategy to alleviate the effect of noise and to further enhance the discriminative power of the feature descriptor. The proposed method is evaluated on our hand vein image database and HK PolyU database. The results with an error equation rate of 0.53% and 0.06% on the two databases, respectively, demonstrate the good performance of our approach.
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