Abstract
This paper proposes to synthesize posed facial images from two parameters for the pose. This parameterization makes the representation, storage, and transmission of face images effective. Because variations of face images show a complicated nonlinear manifold in high- dimensional data space, we use an LLE (Locally Linear Embedding) technique for a good representation of face images. And we apply a snake model to estimate face feature values in the reduced feature space that corresponds to a specific pose parameter. Finally, a synthetic face image is obtained from an interpolation of several neighboring face images. Experimental results show that the proposed method creates an accurate and consistent synthetic face images with respect to changes of pose.
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References
Gong, S., S.J: A. Psarrou, Dynamic Vision (From Images to Face Recognition), Imperial College Press, 2000.
Duda, R.O., Hart, P.E., Stork, D. G.: Pattern Classification, AWiley-Interscience Publication, 2001.
Cox, T., Cox, M.: Multidimensional scaling, Chapman & Hall, 1994.
Roweis, S. T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, Vol.290 22, pp. 2323–2326, 2000.
Saul, L.K., Roweis, S. T.: Think Globally, Fit Locally: Unsupervised Learning of Nonlinear Manifolds, University of Pennsylvania Technical Report, MS-CIS-02-18.
Tenenbaum, J.B., Silva, V., Langford, J. C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, Vol.290 22, pp. 2319–2322, 2000.
Kass, M., Witkin, A.P., Terzopoulos, D.: “Snakes: Active Contour Models”, International Journal of Computer Vision(1), pp. 321–331, 1998.
Willians, D., Shah, M.: A Fast Algorithm for Active Contours and Curvature Estimation, CVGIP: Image Understanding 55, pp. 14–26,1992.
Patch, K.: Tools cut data down to size, Technology Research News, March 14, 2001.
Golub, G. H., Van Loan, C. F.: Matrix Computations, The Johns Hopkins University Press, 1996.
Bregler, C., Omohundro, S.M.: Nonlinear Image Interpolation using Manifold Learning, In Advances in Neural Information Processing Systems 7, 1995.
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Cho, E., Kim, D., Sang-Youn, L. (2003). Posed Face Image Synthesis Using Nonlinear Manifold Learning. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_110
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DOI: https://doi.org/10.1007/3-540-44887-X_110
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