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http://dx.doi.org/10.9728/dcs.2017.18.2.347

Face Recognition via Sparse Representation using the ROMP Method  

Ahn, Jung-Ho (Division of Software Application, Kangnam University)
Choi, KwonTaeg (Division of Software Application, Kangnam University)
Publication Information
Journal of Digital Contents Society / v.18, no.2, 2017 , pp. 347-356 More about this Journal
Abstract
It is well-known that the face recognition method via sparse representation has been proved very robust and showed good performance. Its weakness is, however, that its time complexity is very high because it should solve $L_1$-minimization problem to find the sparse solution. In this paper, we propose to use the ROMP(Regularized Orthogonal Matching Pursuit) method for the sparse solution, which solves the $L_2$-minimization problem with regularization condition using the greed strategy. In experiments, we shows that the proposed method is comparable to the existing best $L_1$-minimization solver, Homotopy, but is 60 times faster than Homotopy. Also, we proposed C-SCI method for classification. The C-SCI method is very effective since it considers the sparse solution only without reconstructing the test data. It is shown that the C-SCI method is comparable to, but is 5 times faster than the existing best classification method.
Keywords
Face recognition; Sparse representation; ROMP; C-SCI; LBP;
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Times Cited By KSCI : 1  (Citation Analysis)
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