Three-dimensional modular discriminant analysis (3DMDA): A new feature extraction approach for face recognition

Computers and Electrical Engineering

Volumn 37, Issue 5, 2011, Pages 811-823

  Safayani, Mehran ^a   Manzuri Shalmani, Mohammad Taghi ^a  

^a SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; DIRECT MODE; EQUAL SIZES; FACE IMAGE DATABASE; FACE IMAGES; FACIAL FEATURE EXTRACTION; MULTILINEAR ALGEBRA; OPTIMIZATION ALGORITHMS; ORIGINAL IMAGES; THIRD-ORDER TENSORS;

ALGEBRA; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; OPTIMIZATION; TENSORS; THREE DIMENSIONAL;

FACE RECOGNITION;

EID: 80455143836     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compeleceng.2011.08.009     Document Type: Article

References (37)

1. I. Joliffe Principal component analysis 1986 Springer Verlag
2. A. Martinez, and A. Kak Pca versus lda IEEE Trans Pattern Anal Mach Intell 23 2001 228 233 (Pubitemid 32254716)
3. M. Turk, A. Pentland, and M. Bsat Face recognition using eigenfaces Comput Vision Pattern Recogn 1991 586 591
4. P.N. Belhumeur, J. Hespanha, and D. Kriegman Eigenfaces vs. fisherfaces: recognition using class specific linear projection IEEE Trans Pattern Anal Mach Intell 19 1997 711 720 (Pubitemid 127762263)
5. J. Ye, C. Janardan, C. Park, and H. Park An optimization criterion for generalized discriminant analysis on undersampled problems IEEE Trans Pattern Anal Mach Intell 26 2004 982 994
6. H. Yu, and J. Yang A direct lda algorithm for high-dimensional data with application to face recognition Pattern Recogn 34 2001 2067 2070
7. N.A. Campbell Canonical variate analysis a general formulation Aust J Stat 26 1984 86 96
8. L. Chen, H. Liao, M. Ko, J. Lin, and G. Yu A new lda-based face recognition system which can solve the small sample size problem Pattern Recogn 33 2000 1713 1726
9. J.H. Friedman Regularized discriminant analysis J Am Stat Assoc 84 1989 165 175
10. T. Hastie, A. Buja, and R. Tibshirani Penalized discriminant analysis Ann Stat 23 1995 73 102
11. H. Gao, and J.W. Davis Why direct lda is not equivalent to lda Pattern Recogn 39 2006 1002 1006 (Pubitemid 43276112)
12. B. Scholkopf, A. Smola, and K.-R. Muller Nonlinear component analysis as a kernel eigenvalue problem Neural Comput 10 1998 1299 1319 (Pubitemid 128463674)
13. G. Baudat, and F. Anouar Generalized discriminant analysis using a kernel approach Neural Comput 12 2000 2385 2404
14. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang Face recognition using laplacianfaces IEEE Trans Pattern Anal Mach Intell 27 2005 328 340 (Pubitemid 40377326)
15. D. Cai, X. He, J. Han, and H.-J. Zhang Orthogonal laplacianfaces for face recognition IEEE Trans Image Process 15 11 2006 3608 3614 (Pubitemid 44615155)
16. J. Yang, D. Zhang, A. Frangi, and J. Yang Two-dimensional pca: a new approach to appearance-based face representation and recognition IEEE Trans Pattern Anal Mach Intell 26 2004 131 137
17. Ye J, Janardan R, Li Q. Two-dimensional linear discriminant analysis. In: Proceedings of the advances in neural information processing systems (NIPS); 2005. p. 1569-76.
18. N. Sun, Z. Ji, and C. Zou Two-dimensional canonical correlation analysis and its application in small sample size face recognition Nueral Comput Appl 19 2010 377 382
19. Safayani M, Manzuri Shalmani M. Matrix-variate probabilistic model for canonical correlation analysis, EURASIP Journal on Advances in Signal Processing 2011, article ID 748430 .
20. Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29.
21. Safayani M, Manzuri Shalmani M. Heteroscedastic multilinear discriminant analysis for face recognition. In: Proceeding of the international conference on pattern recognition (ICPR); 2010. p. 4287-90.
22. Cai D, Xiaofei H, Yuxiao H, Jiawei H, Huang T. Learning a spatially smooth subspace for face recognition. In: Proceedings of the computer vision and pattern recognition (CVPR); 2007. p. 17.
23. H. Wang, and N. Ahuja A tensor approximation approach to dimensionality reduction Int J Comput Vision 76 2008 217 229
24. W. Zheng, J. Lai, and S. Li 1D-lda vs. 2d-lda: When is vector-based linear discriminant analysis better than matrix-based? Pattern Recogn 41 2008 2156 2172
25. E. Kofidis, and P. Regalia On the best rank-1 approximation of higher-order supersymmetric tensors SIAM J Matrix Anal Appl Soc Ind Appl Math 23 2001 863 884
26. L. Lathauwer, B. Moor, and J. Vandewalle A multilinear singular value decomposition SIAM J Matrix Anal Appl Soc Ind Appl Math 21 2000 1253 1278 (Pubitemid 33217068)
27. Rn) approximation of higher-order tensors SIAM J Matrix Anal Appl Soc Ind Appl Math 21 2000 1324 1342 (Pubitemid 33217072)
28. S. Yan, D. Xu, Q. Yang, L. Zhang, X. Tang, and H.J. Zhang Multilinear discriminant analysis for face recognition IEEE Trans Image Process 16 2007 212 220 (Pubitemid 46437483)
29. T. Sim, S. Baker, and M. Bsat The CMU pose, illumination, and expression database IEEE Trans Pattern Anal Mach Intell 25 2003 1615 1618
30. A. Martinez, and R. Benavente The AR face database Tech Rep CVC 24 1998
31. Yale face database. URL .
32. The Japanese female facial expression (JAFFE) database. URL .
33. J. Lu, K. Plataniotis, and A. Venetsanopoulos Regularized discriminant analysis for the small sample size problem in face recognition Pattern Recogn Lett Elsevier Science Inc 24 2003 3079 3087 (Pubitemid 37293335)
34. Lu J, Plataniotis K, Venetsanopoulos A. Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recogn Lett Elsevier Science Inc 2005;26:181-91.
35. G. Golub, and C. Van Loan Matrix computations 3rd ed 1996 The Johns Hopkins University Press
36. Park C, Park H, Pardalos P. A comparative study of linear and nonlinear feature extraction methods. Tech Rep 04-032, Department of Computer Science and Engineering, University of Minnesosta, 2004.
37. Park C, Park H, Pardalos P. A comparative study of linear and nonlinear feature extraction methods. In: Data mining (ICDM); 2004. pp. 495-8. (Pubitemid 40731091)