1D correlation filter based class-dependence feature analysis for face recognition

Authors:
Yan Yan;Yu-Jin Zhang
Affiliations:
Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China and Department of Electronic Engineering, Tsinghua University, Beijing 100084, China;Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China and Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Venue:
Pattern Recognition
Year:
2008

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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.