Abstract
In this paper, we use local linear embedding and linear discriminant analysis for face recognition. Local linear embedding method is used to nonlinearly map high-dimensional face images to low-dimensional feature space. To recover space structure of face images, we use 3D morphable model to derive multiple images of a person from one single image. Experimental results on ORL and UMIST face database show that our method make impressive performance improvement compared with conventional Fisherface method.
Chapter PDF
Similar content being viewed by others
References
R. Chellapa, C. Wilson, S. Sirohey, Human and machine recognition of faces: a survey, Proceedings of the IEEE, 1995, 83(5):705–741.
Sam. T. Roweis, L. K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, Vol 290, 2000: 2323–2326.
T. Hastie, Principal curves and surfaces, Laboratory for computational Statistics Stanford University, Dept. of Statistics Technical Report 11, 1984.
J. B. Tenenbaum, de Silva, V. & Langford, J. C, A global geometric framework for nonlinear dimensionality reduction, Science, Vol 290, 2000: 2319–2323.
Blanz V, Vetter T. A morphable model for the synthesis of 3D faces. In Proceeding of SIGGRAPH’99, Los Angeles: ACM Press, 1999: 187–194.
Yongli Hu, et al, An Improved Morphable Model for 3D Face Synthesis, International Conference on Machine Learning and Cybernetics, Vol. 6, 2004: 4362–4367.
De Ridder, et al, Locally linear embedding for classification, Technical report PH-2002-01, Dept. of Imaging Science & Technology, Delft University of Technology, 2002: 1–15.
P. N. Belhumeur, et al, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. Pattern Anal.Machine Intell, Vol. 19, May 1997: 711–720.
ORL face database. AT&T Laboratories, Cambridge, U. K. [Online]. Available: http://www.cam-orl.co.uk/facedatabase.html
D. B. Graham and N. M. Allinson, Characterizing virtual eigensignatures for general purpose face recognition, in Face Recognition: From Theory to Applications, H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman-Soulie, and T. S. Huang, Eds., 1998, vol. 163, NATO ASI Series F, Computer and Systems Sciences, pp. 446–456.
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
Bai, X., Yin, B., Shi, Q., Sun, Y. (2005). Local Linear Embedding with Morphable Model 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_21
Download citation
DOI: https://doi.org/10.1007/0-387-29295-0_21
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)