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Face Representation And Recognition Using Two-Dimensional PCA

In this paper, two-dimensional principal component analysis (2DPCA) is used for image representation and recognition. Compared to 1D 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 matrices, and its eigenvectors are derived for image feature extraction. In order to test the approach, we have used ORL face database images. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results shows that this approach of extraction of image features is computationally more efficient using 2DPCA than PCA. It is also observed from the results that the recognition rate is high.