Abstract
Recently, neuroscientists emphasized the manifold ways of perception and proposed Isomap for manifold learning. Favorable results have been achieved using Isomap for data description and visualization. However, since the unsupervised Isomap is developed based on minimizing the reconstruction error with multidimensional scaling (MDS) without using the class specific information, it may not be optimal from the perspective of pattern classification. Therefore, an improved version of Isomap, namely SKFD-Isomap, is proposed using class information to construct the neighborhood, and kernel Fisher discriminant (KFD) to achieve the nonlinear embedding. A nearest neighbor classifier is then applied in the subspace for classification. Experimental results show the effectiveness of the proposed approach.
Key words
Download to read the full chapter text
Chapter PDF
References
Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys, 35 (2003), 399–458
Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3 (1991), 72–86
Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: recognition using class specic linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1997), 711–720
Martinez, Aleix M.; Kak, A.C.: Pca versus lda. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23 (2001), 228–233
He, X., Yan, S., Hu, Y., Zhang, H.: Learning a locality preserving subspace for visual recognition. In: Proc. IEEE Ninth International Conference on Computer Vision (ICCV03), Nice, France, (2003), 385–392
Yang, M.H.: Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. In: Proc. IEEE Sixth International Conference on Automatic Face and Gesture recognition (FGR02), Washington D.C. (2002), 215–220
Liu, Q., Huang, R., Lu, H., Ma, S.: Kernel-based nonlinear discriminant analysis for face recognition. In: Proc. IEEE Sixth International Conference on Automatic Face and Gesture recognition (FGR02), Washington D.C. (2002), 788–795
Seung, H.S., Lee, D.D.: The manifold ways of perception. Science, 290 (2000), 2268–2269
Silva, V., Tenenbaum, J., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science, 290 (2000), 2219–2223
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science, 290 (2000), 2223–2226
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, Vancouver, British Columbia, Cannada, (2001), 788–795
Yang, M.H.: Face recognition using extended isomap. In: Proc. IEEE International Conference on Image Processing, Rochester, NY, (2002), 117–120
Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Muller, K.: Fisher discriminant analysis with kernels. In: Proc. IEEE Neural Networks for Signal Processing Workshop, Madison, Wisconsin, (1999), 41–48
Scholkopf, B., Smola, A., Muller, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10 (1998), 1299–1319
Specht, D.F.: A general regression neural network. IEEE Transactions on Neural network, 2 (1991), 568–576
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 International Federation for Information Processing
About this paper
Cite this paper
Li, R., Wang, C., Tu, X. (2005). Skfd-Isomap for Face Recognition. In: Li, D., Wang, B. (eds) Artificial Intelligence Applications and Innovations. AIAI 2005. IFIP — The International Federation for Information Processing, vol 187. Springer, Boston, MA. https://doi.org/10.1007/0-387-29295-0_82
Download citation
DOI: https://doi.org/10.1007/0-387-29295-0_82
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-28318-0
Online ISBN: 978-0-387-29295-3
eBook Packages: Computer ScienceComputer Science (R0)