Classifying faces with discriminant isometric feature mapping

Authors:
Ruifan Li;Cong Wang;Hongwei Hao;Xuyan Tu
Affiliations:
School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;School of Information Engineering, University of Science and Technology Beijing, Beijing, China;,School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Year:
2005

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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.