Kernel Low-Rank Representation for face recognition

Face recognition is one of the fundamental problems of computer vision and pattern recognition. Based on the recent success of Low-Rank Representation (LRR), we propose a novel image classification method for robust face recognition, named Low-Rank Representation-based Classification (LRRC). Based on seeking the lowest-rank representation of a set of test samples with respect to a set of training samples, the algorithm has the natural discrimination to perform classification. We also propose a Kernel Low-Rank Representation-based Classification (KLRRC), which is a nonlinear extension of LRRC. KLRRC is firstly utilized to face recognition. By using the kernel tricks, we implicitly map the input data into the kernel feature space associated with a kernel function. We construct a transformation matrix to reduce the dimensionality of the kernel feature space, where LRRC is performed. Experimental results on several face data sets demonstrate the effectiveness and robustness of our methods.

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