Two Dimension Double PCA for Extract Features and Application
一种双向压缩的二维特征抽取算法及其应用

Because the features extracting by Two Dimension Principal Component Analysis (2DPCA) are matrixes, it needs much space to store these features and slow down the classification speed. We propose a novel feature extraction algorithm called Two Dimension Double PCA (2DDPCA) in this paper. First we use 2DPCA compressing the images in horizon direction, then we compress the features in vertical direction using 2DPCA again. Thus, the dimension of features is lesser and the speed of classification is faster. The experiment on ORL face database indicates that the proposed method outperform 2DPCA.