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1D correlation filter based class-dependence feature analysis for face recognition
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DOI：10.1016/j.patcog.2008.05.028.png)
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Abstract
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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. (C) 2008 Elsevier Ltd. All rights reserved.
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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