A method for face recognition by fusing modular 2DPCA with PCA
HUANG Hai-bo
QUAN Hai-yan
XIE Peng
Abstract：Aiming at the problem that principal component analysis (PCA)leads to a large amount of cal-culation in solving high rank-matrix and the modular two-dimensional principle component analysis (2DPCA)is still large in feature calculation and a certain correlation still exists in feature extraction,a method fusing the Modular 2DPCA with PCA was put forward.The method extracted feature from sub-image using M2DPCA,and re-formed a new matrix according to the order of sub-images of each image,then PCA was carried out on the new matrix.The experimental results in ORL human face database showed that the correlation among feature parameters was removed to a certain extent and it also greatly reduced the dimen-sion of features.
Keywords：modular two-dimensional principle component analysis(M2DPCA)principal component anal-ysis (PCA)feature extractionface recognition
Publication Date：2013-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 81-85 )