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Abstract：In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image atrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.

包含测试样本和训练样本，matlab程序，用lda实现的人脸识别实例，程序注释很清晰，有助于理解算法过程

包含测试样本和训练样本，matlab程序，用pca实现的人脸识别实例，程序注释很清晰，有助于理解算法过程