Kernel correlation filter based redundant class-dependence feature analysis (KCFA) on FRGC2.0 data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3723 LNCS, Issue , 2005, Pages 32-43

  Xie, Chunyan ^a   Savvides, Marios ^a   Vijayakumar, B V K ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONTROL SYSTEMS; DATA ACQUISITION; FACE RECOGNITION; IMAGE ANALYSIS; NONLINEAR SYSTEMS;

CLASS-DEPENDENCE FEATURE ANALYSIS (CFA); FACE RECOGNITION GRAND CHALLENGE (FRGC) DATA; KERNEL CORRELATION FILTER BASED REDUNDANT CLASS-DEPENDENCE FEATURE ANALYSIS (KCFA);

OPTICAL CORRELATION;

EID: 33646402578     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11564386_4     Document Type: Conference Paper

References (21)

1. R. Chellappa, C.L. Wilson, S. Sirohey, "Human and machine recognition of faces: a survey," Proceedings of the IEEE, Vol.83 (5), pp705-741 (1995).
2. R. Gross, J. Shi, and J. Cohn, "Quo Vadis Face Recognition?" Third Workshop on Empirical Evaluation Methods in Computer Vision, December 2001.
3. P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, "Overview of the Face Recognition Grand Challenge," In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2005.
4. M. Turk, A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, 3(1), pp71-86, 1991.
5. A. Pentland, B. Moghaddam, T. Starner, "View-Based and Modular Eigenspaces for Face Recognition," IEEE CVPR, 1994.
6. P.Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. PAMI-19(7),(1997).
7. W. Zheng, C Zou, L. Zhao, "Real-time face recognition using Gram-Schmidt orthogonalization for LDA," ICPR'04, August, 2004, Cambridge UK, pp. 403-406
8. B.V.K. Vijaya Kumar, M. Savvides, K. Venkataramani, and C. Xie, "Spatial Frequency Domain Image Processing For Biometric Recognition," Proceedings IEEE International Conference on Image Processing, pp. 53-56, 2002
9. M. Savvides, B.V.K. Vijaya Kumar, and P.K. Khosla, "'Corefaces'- Robust Shift Invariant PCA based correlation filter for illumination tolerant face recognition," Proceedings IEEE Computer Vision and Pattern Recognition (CVPR). pp. 834-841, June 2004.
10. V. Vapnik, The nature of statistical learning theory, Springer-Verlag, New York, 1995.
11. Schölkopf, A. Smola, and K.-R. Müller. "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, 10:1299-1319, 1998
12. G. Baudat and F. Anouar, "Generalized discriminant analysis using a kernel approach,",Neural Computation, vol. 12, pp. 2385-2404, 2000.
13. N. M. Nasrabadi and H. Kwon, "Kernel spectral matched filter for hyperspectral target detection," Proceedings ICASSP, 665-668., 2005
14. B.V.K. Vijaya Kumar, "Tutorial survey of composite filter designs for optical correlators," Appl. Opt., 31, 4773-4801, (1992)
15. C.F. Hester and D. Casasant, "Multivariant technique for multiclass pattern recognition," Appl. Opt. 21, 4016-4019 (1982)
16. B.V.K. Vijaya Kumar, "Minimum Variance synthetic discriminant functions," J. Opt. Soc. Am A, Vol 3, pp1579-1584.
17. A. Mahalanobis, B.V.K. Vijaya Kumar, and D. Casasent, "Minimum average correlation energy filters," Appl. Opt. 26, pp. 3633-3630 (1987)
18. P. Refregier, "Filter Design for optical pattern recognition: Multi-criteria optimization approach," Opt. Lett., V.15, 854-856, 1990.
19. Chunyan Xie, Marios Savvides and B.V.K. Vijaya Kumar, "Redundant Class-Dependence Feature Analysis Based on Correlation Filters Using FRGC2.0 Data," IEEE Workshop on Face Recognition Grand Challenge Experiments in conjunction with CVPR 2005, San Diego, CA, 21 June 2005
20. B. V. K. Vijaya Kumar and A. Mahalanobis, "Recent advances in composite correlation filter designs," Asian Joural of Physics, Vol. 8, No. 4, pp. 407-420, 1999
21. C. Liu, "Gabor-based Kernel PCA with Fractional Power Polynomial Models for Face Recognition", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 572-581, 2004