Illumination normalization for face recognition and uneven background correction using total variation based image models

Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005

Volumn II, Issue , 2005, Pages 532-539

  Chen, Terrence ^a   Yin, Wotao ^b   Zhou, Xiang Scan ^c   Comaniciu, Dorin ^c   Huang, Thomas S ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b Columbia University ^*  (United States)

^c SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; DATABASE SYSTEMS; DNA; IMAGE ANALYSIS; MATHEMATICAL MODELS; MICROSCOPIC EXAMINATION; OPTIMIZATION; SIGNAL DISTORTION;

DIGITAL MICROSCOPE IMAGES; ILLUMINATION NORMALIZATION; IMAGE MODELS; REFERENCE IMAGES;

FACE RECOGNITION;

EID: 24644479273     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2005.181     Document Type: Conference Paper

References (28)

1. Y. Adini, Y. Moses, and S. Ullman, Face recognition: The problem of compensating for changes in illumination direction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):721-732. 1997.
2. S. Alliney. Digital filters as absolute norm regularize. IEEE Trans, on Signal Processing. 40:1548-1562, 1992.
3. R. Basri and D. Jacobs. Lambertian reflectance and linear subspaces. NEC research institute technical report. 2000.
4. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman. Eigenfaces vs fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(7), 1997.
5. P. N. Belhumeur and D. J. Kriegman. hat is the set of images of an object under all possible lighting conditions? IEEE International Conference on Computer Vision and Pattern Recognition, 1996.
6. 1 function approximation. CLA CAM Report 04-07, 2004.
7. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman. From few to many: Illumination cone models for face recognition under differing pose and lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):643-660, 2001.
8. D. Goldfarb and W. Yin. Second-order cone programming methods for total variation-based image restoration. Columbia CORC TR-2004-05, 2004.
9. G. Guo, S. Z. Li, and K. Chan. Face recognition by support vector machines. FG, pages 196-201, 2000.
10. A. Lanitis, C. J. Taylor, and T. F. Cootes. Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):743-756, 1997.
11. Y. Meyer. Oscillating patterns in image processing. University Lecture Series 22, AMS, 2000.
12. B. Moghaddam, T. Jebara, and A. Pentland. Bayesian face recogntion. Pattern Recognition, .13:1771-1782. 2000.
13. S. K. Nayar and R. M. Bolle. Reflectance based object recognition. International Journal of Computer Vision. 17(3), 1996.
14. M. Nikolova. Minimizers of cost-functions involving nonsmooth data-fidelity terms. SIAM Journal on Numerical Analysis, 40(3):965-994, 2002.
15. P. Oenev and J. Atick. Local feature analysis: A general statistical theory for object reperesentation. Network: Computation in Neural System, 7:477-500, 1996.
16. R. Ramamoorthi and P. Hanrahan. On the relationship between radiance and irradiance: determining the illumination from images of a convex lambertian object. J. Opt. Soc. Am., 18(10), 2001.
17. L. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
18. S. Shan, W. Gao, B. Cao, and D. Zhao. llumination normalization for robust face recognition against varying lighting conditions. IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003.
19. A. Shashua and T. Riklin-Raviv. The quotient image: Classbased re-rendering and recognition with varying illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2): 129-139. 2001.
20. O. D. M. G. W. Site, www.mic-d.com/java/digitalimaging/ backgroundsubtraction/.
21. P. Soille. Morphological Image Analysis: Principles and Applications. New York. 1999.
22. D. Strong and T. Chan. Edge-preserving and scale-dependent properties of total variation regularization. Inverse problems, 19:S165-S187. 2003.
23. M. Turk and A. Pentland. Eigenfaces for recogntion. Journal of cognitive neuroscience, 3(l):71-86. 1991.
24. H. Wang, S. Z. Li, and Y Wang. Generalized quotient image. IEEE International Conference on Computer Vision and Pattern Recognition. 2004.
25. E. Wiskott, J. M. Fellous. N. Kruger, and C. V. D. Malsburg. Face recogntion by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. 19(7):775-779, 1997.
26. Y Zhang. M. Brady. and S. Smith. Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Medical Imaging, 20( l):45-57, 2001.
27. W. Zhao and R. Chellappa. Robust face recognition using symmetric shape-from-shading. Technical report, Center for Automation Research, University of Maryland, 1999.
28. W. Zhao and R. Chellappa. Robust image-based 3d face recognition. CAR-TR-932, N00014-95-1-0521, CS-TR4091, Center for auto research, UMD. 2000.