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A Customized Sparse Representation Model With Mixed Norm for Undersampled Face Recognition
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DOI：10.1109/TIFS.2016.2567318.png)
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Abstract
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In this paper, a customized sparse representation model is proposed to take advantage of the variational information for undersampled face recognition. The proposed model with the mixed norm is a generalization of the extended sparse representation-based classification model. This model guarantees the sparsity of representation coefficient and the robustness for the variational information from generic data set. The mixed norm well fits the distribution of variational information (such as illumination, expression, poses, and occlusion) and the interference information (somewhat face-specific in generic data set) simultaneously. We compare the proposed method with the related methods on several popular face databases, including AR, CMU-PIE, Georgia, and LFW databases. The experimental results show that the proposed method outperforms several popular face recognition methods.
Keywords:
Undersampled face recognition
generic learning
variational information
customized sparse representation
mixed norm
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