Face Recognition Algorithm of 2DPCA Nonparametric Subspace Analysis
Liang Jiu-zhen · Jisuanji gongcheng · 2011
This paper proposes a novel face recognition algorithm of 2D Nonparametric Subspace Analysis(2DNSA) based on 2D Principal Componet Analysis(2DPCA) subspace.The original face matrices are performed to have feature dimension reduction,and the reduced feature matrices are used as a new training set,which can be conducted by 2D non-parametric subspace analysis.This method not only can reduce feature dimensions by 2DPCA,but also consider the impact of boundary samples for classification by taking full advantage of classification capacity of 2DNSA,which avoids the irrationality of using class centers to measure the distances of different classes.Experimental results on the two face databases(namely Yale and LARGE) show the improvements of the developed new algorithm over the traditional subspace methods such as(2D)2PCA,2DPCA,(2D)2LDA,2DLDA,2DPCA+2DLDA,2DNSA,etc.