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to 8 images out performs existing state of the art algorithms. In the future, we
plan to use our database of images to construct the 3D face models for pose
invariant recognition.
Acknowledgments
Thanks to M. Do for the Contourlet Toolbox and R. Owens for the useful dis-
cussions. This research is sponsored by ARC grant DP0881813.
References
1. Arandjelovic, O., Cipolla, R.: Face Recognition from Video Using the Generic
Shape-Illumination Manifold. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV
2006. LNCS, vol. 3954, pp. 27-40. Springer, Heidelberg (2006)
2. Basri, R., Jacobs, D.: Lambertian Reflectance and Linear Subspaces. IEEE Trans.
on PAMI 25(2), 218-233 (2003)
3. Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition
Using Class Specific Linear Projection. IEEE Trans. on PAMI 19, 711-720 (1997)
4. Belhumeur, P., Kriegman, D.: What Is the Set of Images of an Object Under All
Possible Illumination Conditions? Int'l Journal of Computer Vision 28(3), 245-260
(1998)
5. Bowyer, K.W., Chang, K., Flynn, P.: A Survey Of Approaches and Challenges in
3D and Multi-modal 3D + 2D Face Recognition. CVIU 101, 1-15 (2006)
6. Chen, T., Yin, W., Zhou, X., Comaniciu, D., Huang, T.: Total Variation Models
for Variable Lighting Face Recognition. IEEE Trans. on PAMI 28(9), 1519-1524
(2006)
7. Chu, R., Liao, S., Zhang, L.: Illumination Invariant Face Recognition Using Near-
Infrared Images. IEEE Trans. on PAMI 29(4), 627-639 (2007)
8. Do, M.N., Vetterli, M.: The Contourlet Transform: an Ecient Directional Mul-
tiresolution Image Representation. IEEE Trans. on Image Processing 14(12), 2091-
2106 (2005)
9. Georghiades, A., Belhumeur, P., Kriegman, D.: From Few to many: Illumination
cone models for face recognition under variable lighting and pose. IEEE Trans. on
Pattern Analysis and Machine Intell. 6(23), 643-660 (2001)
10. Hillinan, P.: A Low-Dimensional Representation of Human Faces for Arbitrary
Lighting Conditions. In: IEEE Conf. Computer Vision and Pattern Recognition,
pp. 995-999 (1994)
11. Joachims, T.: Making large-Scale SVM Learning Practical. Advances in Kernel
Methods - Support Vector Learning. MIT-Press, Cambridge (1999)
12. Lee, K., Kriegman, D.: Online Probabilistic Appearance Manifolds for Video-based
Recognition and Tracking. In: CVPR, vol. 1, pp. 852-859 (2005)
13. Lee, K., Ho, J., Kriegman, D.: Acquiring Linear Subspaces for Face Recognition
under Variable Lighting. IEEE Trans. on PAMI 27(5), 684-698 (2005)
14. Li, Y., Gong, S., Liddell, H.: Constructing Facial Identity Surfaces for Recognition.
Int. J. Comput. Vision 53(1), 71-92 (2003)
15. Liu, C., Wechsler, H.: Face Recognition Using Independent Gabor Wavelet Fea-
tures. Audio- and Video-Based Biometric Person Auth., 20-25 (2001)
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