${\mathcal {E}}$ and exploit its low-rank property with the tensor nuclear norm. Given that multiple color channels in a color image are generally corrupted at the same positions, we design a tube-wise tailored loss function to further leverage its tube-wise structure. 3) We devise the multi-channel atomic norm (MAN) regularization for the representation coefficient matrix, which allows us to jointly harness the correlation information of coefficients in different color channels. In addition, we also devise an efficient algorithm to solve the TNN-RMAR framework based on the alternating direction method of multipliers (ADMM) framework. By leveraging TNN-RMAR as a general platform, we also develop several novel robust multi-channel RC methods. Experimental results on benchmark real-world databases validate the effectiveness and robustness of the proposed framework for robust color face recognition.">

Tensor Nuclear Norm-Based Multi-Channel Atomic Representation for Robust Face Recognition (original) (raw)

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