Abstract
Dictionary learning (DL) technique has received a great interest recently, due to its significant role in feature extraction. Although many DL-based methods have been presented, some of them still suffer from the lack of discriminative features, especially for the local manifold features. To mitigate this problem, the authors propose a novel DL method named low-rank representation based on twin tensor kernel (LRR-TTK) DL for face recognition in this study. Specifically, the training samples are projected to a high-dimensional space with TTK. Then, they extract the local manifold features and spatial features (representation coefficients) hidden in the facial images by TT locality preserving projection. In addition, powered by LRR reconstruction and DL theory, much more discriminative features are obtained, which can improve the recognition rate greatly. Comprehensive experimental results at AR, extended Yale-B and FERET face databases demonstrate the superiority of their proposed method.
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