Choosing parameters of kernel subspace LDA for recognition of face images under pose and illumination variations

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 37, Issue 4, 2007, Pages 847-862

  Huang, Jian ^a,b   Yuen, Pong C ^a   Chen, Wen Sheng ^c   Lai, Jian Huang ^b  

^a HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

^b SUN YAT SEN UNIVERSITY   (China)

^c SHENZHEN UNIVERSITY   (China)

Author keywords

Gaussian radial basis function (RBF) kernel; Generalization capability; Kernel Fisher discriminant (KFD); Kernel parameter; Model selection

Indexed keywords

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; EIGENVALUES AND EIGENFUNCTIONS; FEATURE EXTRACTION; LEARNING SYSTEMS; LIGHTING; PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS;

GAUSSIAN RADIAL BASIS FUNCTION (RBF) KERNEL; KERNEL PARAMETER; MODEL SELECTION;

FACE RECOGNITION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOMETRY; BODY POSTURE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; EVALUATION; FACE; HISTOLOGY; HUMAN; ILLUMINATION; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; COMPUTER SIMULATION; DISCRIMINANT ANALYSIS; FACE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LIGHTING; PATTERN RECOGNITION, AUTOMATED; POSTURE;

EID: 34547099832     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2007.895328     Document Type: Article

References (43)

1. R. Chellappa, C. Wilson, and S. Sirohey, "Human and machine recognition of faces: A survey," Proc. IEEE, vol. 83, no. 5, pp. 705-740, May 1995.
2. W. Zhao, R. Chellappa, A. Rosenfeld, and P. Phillips, "Face recognition: A literature survey," ACM Comput. Surv., vol. 35, no. 4, pp. 399-458, Dec. 2003.
3. M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cogn. Neurosci., vol. 3, no. 1, pp. 71-86, 1991.
4. A. M. Martinez and A. C. Kak, "PCA versus LDA," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 2, pp. 228-233, Feb. 2001.
5. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711-720, Jul. 1997.
6. D. L. Swets and J. Weng, "Using discriminant eigenfeatures for image retrieval," IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 831-836, Aug. 1996.
7. B. Schölkopf, A. Smola, and K. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," MPI fur biologische kybernetik, Tubingen, Germany, Tech. Rep. 44, 1996.
8. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, and K. R. Müller, "Fisher discriminant analysis with kernels," in Proc. IEEE Workshop Neural Netw. Signal Process. IX, 1999, pp. 41-48.
9. G. Baudat and F. Anouar, "Generalized discriminant analysis using a kernel approach," Neural Comput., vol. 12, no. 10, pp. 2385-2404, 2000.
10. M. H. Yang, "Kernel eigenfaces vs. kernel Fisherfaces: Face recognition using kernel methods," in Proc. 5th IEEE Int. Conf. Autom. Face and Gesture Recog., 2002, pp. 215-220.
11. J. W. Lu, K. Plataniotis, and A. N. Venetsanopoulos, "Face recognition using kernel direct discriminant analysis algorithms," IEEE Trans. Neural Netw., vol. 14, no. 1, pp. 117-126, Jan. 2003.
12. W. Zheng, L. Zhao, and C. Zou, "A modified algorithm for generalized discriminant analysis," Neural Comput., vol. 16, no. 6, pp. 1283-1297, 2004.
13. J. Huang, P. C. Yuen, W. S. Chen, and J. H. Lai, "Component-based subspace LDA method for face recognition with one training sample," Opt. Eng., vol. 44, no. 5, p. 057 002, 2005.
14. B. Moghaddam, T. Jebara, and A. Pentland, "Bayesian face recognition," Pattern Recognit., vol. 33, no. 11, pp. 1771-1782, 2000.
15. W. Zhao, R. Chellappa, and P. Phillips, "Subspace linear discriminant analysis for face recognition," Center Autom. Res., Univ. Maryland, College Park, MD, Tech. Rep. CAR-TR-914, 1999.
16. H. Yu and J. Yang, "A direct LDA algorithm for high-dimensional data - With application to face recognition," Pattern Recognit., vol. 34, no. 10, pp. 2067-2070, 2001.
17. L. F. Chen, H. Y. Liao, M. T. Ko, J. C. Lin, and G. J. Yu, "A new LDA-based face recognition system which can solve the small sample size problem," Pattern Recognit., vol. 33, no. 10, pp. 1713-1726, 2000.
18. W. Zheng, L. Zhao, and C. Zou, "An efficient algorithm to solve the small sample size problem for LDA," Pattern Recognit., vol. 37, no. 5, pp. 1077-1079, 2004.
19. R. Huang, Q. S. Liu, H. Q. Lu, and S. D. Ma, "Solving small sample size problem in LDA," in Proc. Int. Conf. Pattern Recog., Aug. 2002, vol. 3, pp. 29-32.
20. J. Yang, J.-Y. Yang, and D. Zhang, "What's wrong with Fisher criterion?" Pattern Recognit., vol. 35, no. 11, pp. 2665-2668, 2002.
21. J. Yang and J.-Y. Yang, "Why can LDA be performed in PCA transformed space?" Pattern Recognit., vol. 36, no. 2, pp. 563-566, 2003.
22. J. Yang, J.-Y. Yang, and A. Frangi, "Combined Fisherfaces framework," Image Vis. Comput., vol. 21, no. 12, pp. 1037-1044, 2003.
23. J. Yang, A. Frangi, and J.-Y. Yang, "A new kernel Fisher discriminant algorithm with application to face recognition," Neurocomputing, vol. 56, no. 1, pp. 415-421, 2004.
24. J. Yang, Z. Jin, J. Yang, D. Zhang, and A. F. Frangi, "Essence of kernel Fisher discriminant: KPCA plus LDA," Pattern Recognit., vol. 37, no. 10, pp. 2097-2100, 2004.
25. J. Yang, D. Zhang, and J.-Y. Yang, "A generalised k-l expansion method which can deal with small sample size and high-dimensional problems," Pattern Anal. Appl., vol. 6, no. 1, pp. 47-54, 2003.
26. M. Wilkes, A. Barkana, H. Cevikalp, and M. Neamtu, "Discriminative common vectors for face recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 1, pp. 4-13, Jan. 2005.
27. J. Liu and S. Chen, "Discriminant common vectors versus neighbourhood components analysis and Laplacianfaces: A comparative study in small sample size problem," Image Vis. Comput., vol. 24, no. 3, pp. 249-262, 2006.
28. C. Liu and H. Wechsler, "Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition," IEEE Trans. Image Process., vol. 11, no. 4, pp. 467-476, Apr. 2002.
29. T. Jaakkola and D. Haussler, "Probabilistic kernel regression models," in Proc. Conf. AI and Statist., 1999.
30. O. Chapelle, V. Vapnik, O. Bousquet, and S.Mukherjee, "Choosing multiple parameters for support vector machines," Mach. Learn., vol. 46, no. 1, pp. 131-159, 2002.
31. S. Mika, "Kernel fisher discriminants," Ph.D. dissertation, Tech. Univ. Berlin, School Electr. Eng. Comput. Sci., Berlin, Germany, Dec 2002.
32. K. Schittkowski, "Optimal parameter selection in SVM," J. Ind. Manag. Optim., vol. 1, no. 4, pp. 465-476, 2005.
33. J.-H. Lee and C.-J. Lin, "Automatic model selection for support vector machines," Dept. Comput. Sci. Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan, Tech. Rep., 2000.
34. S. Keerthi and C. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural Comput., vol. 15, no. 7, pp. 1667-1689, Jul. 2003.
35. D.-Q. Zhang, S. Chen, and Z.-H. Zhou, "Learning the kernel parameters in kernel minimum distance classifier," Pattern Recognit., vol. 39, no. 1, pp. 133-135, 2006.
36. N. Cristianini, J. Shawe-Taylor, and A. Elisseeff, "On kernel-target alignment," in Proc. Neural Inf. Process. Syst., 2001, pp. 367-373.
37. H. Xiong, M. N. S. Swamy, and M. O. Ahmad, "Optimizing the kernel in the empirical feature space," IEEE Trans. Neural Netw., vol. 16, no. 2, pp. 460-474, Mar. 2005.
38. J. Huang, P. C. Yuen, W. S. Chen, and J. H. Lai, "Face recognition using kernel subspace-LDA algorithm," in Proc. Asian Conf. Comput. Vis., Jan. 2004, vol. 1, pp. 61-66. no. 10.
39. H. Li, T. Jiang, and K. Zhang, "Efficient and robust feature extraction by maximum margin criterion," in Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004.
40. O. Bousquet and A. Elisseeff, "Stability and generalization," J. Mach. Learn. Res., vol. 2, no. 3, pp. 499-526, 2002.
41. W. H. Rogers and T. J. Wagner, "A finite sample distribution-free performance bound for local discrimination rules," Ann. Stat., vol. 6, no. 3, pp. 506-514, May 1978.
42. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, "The FERET evaluation methodology for face-recognition algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1090-1104, Oct. 2000.
43. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, "From few to many: Illumination cone models for face recognition under variable lighting and pose," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 643-660, Jun. 2001.