(2D)PCA: 2-Directional 2-Dimensional PCA for Efficient Face Representation and Recognition

Recently, a new technique called 2-dimensional principal component analysis (2DPCA) was proposed for face representation and recognition. The main idea behind 2DPCA is that it is based on 2D matrices as opposed to the standard PCA, which is based on 1D vectors. Although 2DPCA obtains higher recognition accuracy than PCA, a vital unresolved problem of 2DPCA is that it needs many more coefficients for image representation than PCA. In this paper, we first indicate that 2DPCA is essentially working in the row direction of images, and then propose an alternative 2DPCA which is working in the column direction of images. By simultaneously considering the row and column directions, we develop the 2-Directional 2DPCA, i.e. (2D)PCA, for efficient face representation and recognition. Experimental results on ORL and a subset of FERET face databases show that (2D)PCA achieves the same or even higher recognition accuracy than 2DPCA, while the former needs a much reduced coefficient set for image representation than the latter.