Tensor 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:
Neurocomputing
Year:
2008

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Abstract

Recently, class-dependence feature analysis (CFA), which is based on the design of correlation filters in the frequency domain, has been developed for robust face recognition. Traditional CFA designs correlation filters by using two-dimensional (2D) Fourier transforms of the images. In this paper, we propose a tensor correlation filter based CFA (TCF-CFA) method to generalize traditional CFA by encoding the image data as tensors. Experimental results on four benchmark face databases show the effectiveness and robustness of TCF-CFA for face recognition.