A method for face recognition by fusing modular 2DPCA with PCA

Aiming at the problem that principal component analysis(PCA) leads to a large amount of calculation 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 dimension of features.