1D correlation filter based class-dependence feature analysis for face recognition |
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Authors: | Yan Yan [Author Vitae] Yu-Jin Zhang [Author Vitae] |
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Affiliation: | a Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China b Department of Electronic Engineering, Tsinghua University, Beijing 100084, China |
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Abstract: | In this paper, a novel one-dimensional correlation filter based class-dependence feature analysis (1D-CFA) method is presented for robust face recognition. Compared with original CFA that works in the two dimensional (2D) image space, 1D-CFA encodes the image data as vectors. In 1D-CFA, a new correlation filter called optimal extra-class origin output tradeoff filter (OEOTF), which is designed in the low-dimensional principal component analysis (PCA) subspace, is proposed for effective feature extraction. Experimental results on benchmark face databases, such as FERET, AR, and FRGC, show that OEOTF based 1D-CFA consistently outperforms other state-of-the-art face recognition methods. This demonstrates the effectiveness and robustness of the novel method. |
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Keywords: | Correlation filter Optimal extra-class origin output tradeoff filter (OEOTF) Class-dependence feature analysis (CFA) Linear subspace learning Face recognition |
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