A novel class-dependence feature analysis method for face recognition
作者： Yan Yan , Yu-Jin Zhang
DOI: 10.1016/J.PATREC.2008.04.018
关键词:
摘要: 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 (STSA). Designing filters 2-D makes them more tolerant to distortions illumination and facial expression etc. Moreover, focusing on outputs at origin, OOCTF very effective vector extraction. Experimental results three benchmark databases show superiority of proposed over traditional recognition methods.
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