Block-wise two-directional 2DPCA with ensemble learning for face recognition

Two-Dimensional Principal Component Analysis (2DPCA) is a well-known feature extraction method for face recognition. One of the main drawbacks of this method, in comparison with the vector-based PCA, is that it needs many more coefficients to represent the feature matrix of an image. Two-Directional 2DPCA ((2D)2PCA), proposed in the literature, attempts to alleviate this problem. However, it fails to improve the recognition accuracy of 2DPCA. In addition, (2D)2PCA follows a global feature extraction approach that might fail to preserve some important local features. In this paper, we propose Block-Wise (2D)2PCA to enhance the performance of (2D)2PCA by preserving the local informative variations. On average, the feature matrices produced by the proposed method and those formed by (2D)2PCA are about the same size. However, our experiments on four face recognition databases indicate that our method is superior to (2D)2PCA in terms of the recognition accuracy.