A novel class-dependence feature analysis method 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 Letters
Year:
2008

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Abstract

This paper develops a novel Class-dependence Feature Analysis (CFA) method for robust face recognition. A new correlation filter called Optimal Origin Correlation output Tradeoff Filter (OOCTF) is designed in the two-dimensional (2-D) feature space obtained by Second-order Tensor Subspace Analysis (STSA). Designing correlation filters in the 2-D feature space makes them more tolerant to distortions in illumination and facial expression etc. Moreover, by focusing on the correlation outputs at the origin, OOCTF is very effective for feature vector extraction. Experimental results on three benchmark face databases show the superiority of the proposed method over traditional face recognition methods.