The Dimension Reduction Method of Face Feature Parameters Based on Modular 2DPCA and PCA

Abstract

In face recognition algorithms, Principal Component Analysis (PCA) is one of classical algorithms. But PCA algorithm needs to convert each sample matrix into vectors, which leads to a large amount of calculations in solving high-rank matrix. The essence of Modular Two-dimensional Principle Component Analysis (2DPCA) is that original images are divided into modular images, and image covariance matrix is constructed directly from the sub-images by the optimal projection matrix. But the number of features is still large and correlation still exists in feature extraction, which influences the speed of classification. In order to solve this problem, we proposed a method combining the Modular 2DPCA with PCA to reduce the dimension of features and decrease the correlation among feature parameters. The experimental results based on ORL Human Face Database show that the recognition rate of the algorithm is superior to single Modular 2DPCA or PCA.