Linear projection methods in face recognition under unconstrained illuminations: A comparative study

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2, Issue , 2004, Pages

  Li, Qi ^a   Ye, Jieping ^b   Kambhamettu, Chandra ^a  

^a UNIVERSITY OF DELAWARE   (United States)

^b UNIVERSITY OF MINNESOTA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECOMPOSITION ANALYSIS; LINEAR PROJECTION METHODS; UNCONSTRAINED ILLUMINATIONS;

LINEAR PROGRAMMING; MATRIX ALGEBRA; PRINCIPAL COMPONENT ANALYSIS; PROBLEM SOLVING; STATISTICAL METHODS;

FACE RECOGNITION;

EID: 5044235418     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. R. Basri and D. Jacobs. Lambertian reflectance and linear subspaces. IEEE TPAMI, 25(2):218-233, 2003.
2. P. Belhumeur, J. Hespanha, and D. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. In ECCV(1), pages 45-58, 1996.
3. J. Chien and C. Wu. Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE TPAMI, 24(22):1644-1649, 2002.
4. I. Daubechies. Ten lectures on wavelets. SIAM, Philadelphia, 1992.
5. R. Epstein, P. Hallinan, and A. Yuille. 5 ± 2 eigenimages suffice: An empirical investigation of low-dimensional lighting models. In IEEE workshop on physics-based modeling in computer vision, pages 108-116, 1994.
6. A. Georghiades, P. Belhumeur, and D. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE TPAMI, 23(6):643-660, 2001.
7. P. Hallinan. A low-dimensional representation of human faces for arbitrary lighting conditions. In CVPR, pages 995-999, 1994.
8. K. Lee, J. Ho, and D. Kriegman. Ninepoints of light: Acquiring subspaces for face recognition under variable lighting. In IEEE Conf. CVPR, pages 519-526, 2001.
9. S. Mallat. A wavelet tour of signal processing. Academic Press, 1998.
10. H. Park, P. Rowland, and M. Jeon. Cluster structure preserving dimension reduction based on the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 25(1):165-179, 2003.
11. H. Park, M. Jeon, and J. Rosen. Lower dimensional representation of text data based on centroids and least squares. BIT, 43(2):1-22, 2003.
12. R. Ramamoorthi. Analytic PCA construction for theoretical analysis of lighting variability, including attached shadows, in a single image of a convex lambertian object. IEEE TPAMI, 24(10):1322-1333, 2002.
13. T. Sim, S. Baker, and M. Bsat. The CMU pose, illumination, and expression (PIE) database. In Proc. 4th Intl. Conf. on FG'02.
14. D. Swets and J. Weng. Using discriminant eigenfeatures for image retrieval. IEEE TPAMI, 18(8):831-836, 1996.
15. M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71-86, 1991.
16. J. Ye, R. Janardan, C. park, and H. Park. An optimization criterion for generalized discriminant analysis on undersampled problems. IEEE TPAMI, accepted.
17. J. Ye and Q. Li. LDA/QR: An efficient and effective dimension reduction algorithm and its theoretical foundation. Pattern Recognition, 37(4):851-854, 2004.
18. L. Zhang and D. Samaras. Face recognition under variable lighting using harmonic image exemplars. In IEEE Conf. CVPR, volume I, pages 19-25, 2003.
19. W. Zhao, A. Krishnaswamy, R. Chellappa, D. Swets, and J. Weng. Discriminant analysis of principal components for face recognition. In Wechsler, Phillips, Bruce, Fogelman-Soulie, and Huang, editors, Face Recognition: From Theory to Applications, pages 73-85 Springer- Verlag, 1998.