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- W2553463980 abstract "Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved." @default.
- W2553463980 created "2016-11-30" @default.
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- W2553463980 date "2016-01-01" @default.
- W2553463980 modified "2023-09-26" @default.
- W2553463980 title "Robust Face Recognition Via Gabor Feature and Sparse Representation" @default.
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- W2553463980 doi "https://doi.org/10.1051/itmconf/20160702004" @default.
- W2553463980 hasPublicationYear "2016" @default.
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