Classifying faces with discriminant isometric feature mapping

Abstract

Recently proposed manifold learning algorithms, e.g. Isometric feature mapping (Isomap), Locally Linear Embedding (LLE), and Laplacian Eigenmaps, are based on minimizing the construction error for data description and visualization, but not optimal from classification viewpoint. A discriminant isometric feature mapping for face recognition is presented in this paper. In our method, the geodesic distances between data points are estimated by Floyd's algorithm, and Kernel Fisher Discriminant is then utilized to achieve the discriminative nonlinear embedding. Prior to the estimation of geodesic distances, the neighborhood graph is constructed by incorporating class information. Experimental results on two face databases demonstrate that the proposed algorithm achieves lower error rate for face recognition. © Springer-Verlag Berlin Heidelberg 2005.