Kernel Orthogonal Unsupervised Discriminant Projection with Applications to Face Recognition

In view of the problems of feature extraction in face recognition,an improved version of unsupervised discriminant projection(UDP) named kernel orthogonal unsupervised discriminant projection is proposed in this paper.First the nonlinear information in face images is extracted by the kernel trick and mapped into a high dimensional nonlinear space.Then a linear transformation which produces orthogonal basis vectors is performed to preserve locality of the geometric structure of the face images.The kernel trick helps obtain nonlinear structure features and the orthogonal basis vectors help preserve the information of nonlinear sub-manifold space related to the metric structure.Experiments on ORL and PIE face database demonstrate the effectiveness of the proposed algorithm.