Subspace method for efficient face recognition using a combination of radon transform and KL expansion
This paper describes an efficient face recognition method using a combination of the Radon transform and the KL expansion. In this paper, each facial image is transformed into many sets of line integrals resulting from the Radon transform in 2D space. Based on this transformation, a new face-recognition method is proposed by using many subspaces generated from the vector spaces of the Radon transform. The efficiencies of the proposed method are proved by the classification rate of 100% in the experimental results, and the reduction to O(n(4)) instead of O(n(6)) of the operation complexity in KL(Karhunen-Loeve) expansion, where n is the size of sample images.
subspaces radon transform kl transform eigenvectors classification confidence