Representation of face images for personal authentication

In this paper, we propose to use all the training samples in the original space or in the transform space to represent and classify test samples. It is shown that this method somewhat possesses some of the properties of sparseness. In other words, a large portion of the solution components have very small absolute values and only a few have large absolute values. Our analysis mathematically partially supports this claim of sparseness. We also explore other characteristics of the proposed method and compare the proposed sample representation method with transform methods that are based on conventional coordinate axes. The proposed method performs better than the state-of-the-art face recognition methods. Further, our method can be solved at a low computational cost. Its algorithm is simple and easy to understand, and its classification procedure is intuitive. The performance of our method is shown by a large number of face recognition experiments.