IEEE Trans Image Process - Robust face recognition with structurally incoherent low-rank matrix decomposition.

Tópicos

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Resumo

For the task of robust face recognition, we particularly focus on the scenario in which training and test image data are corrupted due to occlusion or disguise. Prior standard face recognition methods like Eigenfaces or state-of-the-art approaches such as sparse representation-based classification did not consider possible contamination of data during training, and thus their recognition performance on corrupted test data would be degraded. In this paper, we propose a novel face recognition algorithm based on low-rank matrix decomposition to address the aforementioned problem. Besides the capability of decomposing raw training data into a set of representative bases for better modeling the face images, we introduce a constraint of structural incoherence into the proposed algorithm, which enforces the bases learned for different classes to be as independent as possible. As a result, additional discriminating ability is added to the derived base matrices for improved recognition performance. Experimental results on different face databases with a variety of variations verify the effectiveness and robustness of our proposed method.

Resumo Limpo

task robust face recognit particular focus scenario train test imag data corrupt due occlus disguis prior standard face recognit method like eigenfac stateoftheart approach spars representationbas classif consid possibl contamin data train thus recognit perform corrupt test data degrad paper propos novel face recognit algorithm base lowrank matrix decomposit address aforement problem besid capabl decompos raw train data set repres base better model face imag introduc constraint structur incoher propos algorithm enforc base learn differ class independ possibl result addit discrimin abil ad deriv base matric improv recognit perform experiment result differ face databas varieti variat verifi effect robust propos method

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