Title: Face Recognition Based on Dynamic Principal Component Subspace
Abstract: The significance of principal component was determined by the corresponding eigenvalue in face recognition based on subspace analysis,then a static feature subspace was established.However,it could result in an inaccurate performance by analyzing the process of face reconstruction.A dynamic feature subspace algorithm was proposed according to multiple linear regression analysis.Furthermore,a dynamic principal component analysis(DPCA)and a Gabor feature based dynamic principal component analysis(GDPCA)were brought forward.Experiment results on ORL and Georgia Tech face databases show that the proposed algorithm not only decrease the number of principal components but also increase the correct rate of face recognition.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
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