Two-Dimensional Random Projection for Face Recognition

In this paper, the two-dimensional random projection (2DRP) is proposed to directly project the image matrix from high-dimensional space to low-dimensional space for recognition task. In traditional random projection framework, the projection matrix does not depend on the training data hence it can avoid the principal classification problems such as over-fitting, Small Sample Size (SSS), and singularity problems. However, the face images must be transformed to vectors before projection. In this way, the size of projection matrix will depend on the product of width and height of the image, that is very large and consumes lots of memory and computation time to process. Instead of the traditional projection, our method uses unilateral and bilateral projection for an image matrix directly, which applies the left and right projections to each image matrix side by side or simultaneously. Thus, the size of left and right projections will depend on only the height or width of an image, respectively. The memory and computation time of this method will be substantially reduced. After projection, we investigate the results of 2DRP by the nearest neighbor classifier. Our experiments on well-known face databases demonstrate the significant of our proposed method.