Nearest manifold approach for face recognition

Faces under varying illumination, pose and non-rigid deformation are empirically thought of as a highly nonlinear manifold in the observation space. How to discover intrinsic low-dimensional manifold is important to characterize meaningful face distributions and classify them using a simpler, such as linear or Gaussian based, classifier. In this paper, we present a manifold learning algorithm (MLA) for learning a mapping from highly-dimensional manifold into the intrinsic low-dimensional linear manifold. We also propose the nearest manifold (NM) criterion for the classification and present an algorithm for computing the distance from the sample to be classified to the nearest face manifolds in light of local linearity of manifold. Based on these works, face recognition is achieved with the combination of MLA and NM. Experiments on several face databases show that the advantages of our proposed combinational approach.