Compressed sensing for face recognition

In this paper, we present a new approach to build a more robust and efficient face recognition system. The idea is to fit the face recognition task into the new mathematical theory and algorithm of compressed sensing framework. With its beautiful theoretical results from compressed sensing, the new face recognition framework stably gives a better performance with some advantages compared to traditional approaches. Experimental results show the promising aspects of new approach when comparing with the most popular subspace analysis approaches in face recognition such as Eigenfaces, Fisherfaces, and Laplacianfaces in terms of recognition accuracy, efficiency, and numerical stability.