IEEE Trans Image Process - Regularized robust coding for face recognition.

Tópicos

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Resumo

Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1 -norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR(3)C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.

Resumo Limpo

recent spars represent base classif src propos robust face recognit fr src test imag code spars linear combin train sampl represent fidel measur lnorm l norm code residu spars code model assum code residu follow gaussian laplacian distribut may effect enough describ code residu practic fr system meanwhil sparsiti constraint code coeffici make comput cost src high paper propos new face code model name regular robust code rrc robust regress given signal regular regress coeffici assum code residu code coeffici respect independ ident distribut rrc seek maximum posterior solut code problem iter reweight regular robust code irc algorithm propos solv rrc model effici extens experi repres face databas demonstr rrc much effect effici stateoftheart spars represent base method deal face occlus corrupt light express chang etc

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