IEEE Trans Pattern Anal Mach Intell - Maximum Correntropy Criterion for Robust Face Recognition.

Tópicos

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Resumo

In this paper, we present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l(1)norm-based sparse representation classifier (SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In order to develop a more tractable and practical approach, we in particular impose nonnegativity constraint on the variables in the maximum correntropy criterion and develop a half-quadratic optimization technique to approximately maximize the objective function in an alternating way so that the complex optimization problem is reduced to learning a sparse representation through a weighted linear least squares problem with nonnegativity constraint at each iteration. Our extensive experiments demonstrate that the proposed method is more robust and efficient in dealing with the occlusion and corruption problems in face recognition as compared to the related state-of-the-art methods. In particular, it shows that the proposed method can improve both recognition accuracy and receiver operator characteristic (ROC) curves, while the computational cost is much lower than the SRC algorithms.

Resumo Limpo

paper present spars correntropi framework comput robust spars represent face imag recognit compar stateoftheart lnormbas spars represent classifi src assum nois also spars represent spars algorithm develop base maximum correntropi criterion much insensit outlier order develop tractabl practic approach particular impos nonneg constraint variabl maximum correntropi criterion develop halfquadrat optim techniqu approxim maxim object function altern way complex optim problem reduc learn spars represent weight linear least squar problem nonneg constraint iter extens experi demonstr propos method robust effici deal occlus corrupt problem face recognit compar relat stateoftheart method particular show propos method can improv recognit accuraci receiv oper characterist roc curv comput cost much lower src algorithm

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