Two-Dimensional Maximum Margin Feature Extraction for Face Recognition

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

Volumn 39, Issue 4, 2009, Pages 1002-1012

  Yang, Wen Hui ^a   Dai, Dao Qing ^a  

^a SUN YAT SEN UNIVERSITY   (China)

Author keywords

2 D discriminant analysis (2DDA); Face recognition; feature extraction; Fisher discriminant analysis; linear discriminant analysis (LDA); maximal margin criterion (MMC)

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOMETRY; DISCRIMINANT ANALYSIS; FACE; HISTOLOGY; HUMAN; IMAGE PROCESSING; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY;

ALGORITHMS; BIOMETRY; DISCRIMINANT ANALYSIS; FACE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PRINCIPAL COMPONENT ANALYSIS; REPRODUCIBILITY OF RESULTS;

EID: 70149083585     PISSN: 10834419     EISSN: 19410492     Source Type: Journal    
DOI: 10.1109/TSMCB.2008.2010715     Document Type: Article

References (33)

1. Y. H. Liu and Y. T. Chen, “Face recognition using total margin-based adaptive fuzzy support vector machines,” IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 178–192, Jan. 2007.
2. A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 4–20, Jan. 2004.
3. W. Zhao, R. Chellappa, and P. J. Phillips et al., “Face recognition: A literature survey,” ACM Comput. Surv., vol. 35, no. 4, pp. 399–459, Dec. 2003.
4. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
5. M. A. Turk and A. P. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci., vol. 3, no. 1, pp. 71–86, 1991.
6. 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.
7. P. Howland, J. Wang, and H. Park, “Solving the small sample size problem in face recognition using generalized discriminant analysis,” Pattern Recognit., vol. 39, no. 2, pp. 277–287, Feb. 2006.
8. W. J. Krzanowski, P. Jonathan, W. V. McCarthy, and M. R. Thomas, “Discriminant analysis with singular covariance matrices: Methods and applications to spectroscopic data,” Appl. Stat., vol. 44, no. 1, pp. 101–115, 1995.
9. S. J. Raudys and A. K. Jain, “Small sample size effects in statistical pattern recognition: Recommendations for practitioners,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 3, pp. 252–264, Mar. 1991.
10. D. Q. Dai and P. C. Yuen, “Regularized discriminant analysis and its application to face recognition,” Pattern Recognit., vol. 36, no. 3, pp. 845–847, Mar. 2003.
11. D. Q. Dai and P. C. Yuen, “Face recognition by regularized discriminant analysis,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 4, pp. 1080–1085, Aug. 2007.
12. X. S. Zhuang and D. Q. Dai, “Inverse Fisher discriminate criteria for small sample size problem and its application to face recognition,” Pattern Recognit., vol. 38, no. 11, pp. 2192–2194, Nov. 2005.
13. X. S. Zhuang and D. Q. Dai, “Improved discriminate analysis for high-dimensional data and its application to face recognition,” Pattern Recognit., vol. 40, no. 5, pp. 1570–1578, May 2007.
14. M. Kyperountas, A. Tefas, and I. Pitas, “Weighted piecewise LDA for solving the small sample size problem in face verification,” IEEE Trans. Neural Netw., vol. 18, no. 2, pp. 506–519, Mar. 2007.
15. S. Raudys and R. P. W. Duin, “Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix,” Pattern Recognit. Lett., vol. 19, no. 5/6, pp. 385–392, Apr. 1998.
16. 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 Recognit., vol. 33, no. 10, pp. 1713–1726, Oct. 2000.
17. 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, Oct. 2001.
18. H. Li, T. Jiang, and K. Zhang, “Efficient and robust feature extraction by maximum margin criterion,” IEEE Trans. Neural Netw., vol. 17, no. 1, pp. 157–165, Jan. 2006.
19. M. Li and B. Yuan, “2D-LDA: A statistical linear discriminant analysis for image matrix,” Pattern Recognit. Lett., vol. 26, no. 5, pp. 527–532, Apr. 2005.
20. J. Yang and J. Y. Yang, “From image vector to matrix: A straightforward image projection technique—IMPCA vs. PCA,” Pattern Recognit., vol. 35, no. 9, pp. 1997–1999, Sep. 2002.
21. J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-dimensional PCA: A new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 1, pp. 131–137, Jan. 2004.
22. J. Yang, D. Zhang, X. Yong, and J. Y. Yang, “Two-dimensional discriminant transform for face recognition,” Pattern Recognit., vol. 38, no. 7, pp. 1125–1129, Jul. 2005.
23. 2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition,” Neurocomputing, vol. 69, no. 1–3, pp. 224–231, Dec. 2005.
24. 2LDA: An efficient approach for face recognition,” Pattern Recognit., vol. 39, no. 7, pp. 1396–1400, Jul. 2006.
25. J. Ye, “Generalized low rank approximations of matrices,” Mach. Learn., vol. 61, no. 1–3, pp. 167–191, Nov. 2005.
26. J. Ye, R. Janardan, and Q. Li, “Two-dimensional linear discriminant analysis,” in Advances in Neural Information Processing Systems, vol. 17. Cambridge, MA: MIT Press, 2005, pp. 1569–1576.
27. X. Y. Jing, H. S. Wong, and D. Zhang, “Face recognition based on 2D Fisherface approach,” Pattern Recognit., vol. 39, no. 4, pp. 707–710, Apr. 2006.
28. W. M. Zuo, D. Zhang, J. Yang, and K. Wang, “BDPCA plus LDA: A novel fast feature extraction technique for face recognition,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 4, pp. 946–953, Aug. 2006.
29. C. K. Chen and J. Y. Yang, “Two-dimensional discriminant transform based on scatter difference criterion for face recognition,” in Advances in Natural Computation, vol. 4222. Berlin, Germany: Springer-Verlag, 2006, pp. 683–686.
30. X. Qiu and L. Wu, “Nonparametric maximum margin criterion for face recognition,” in Proc. IEEE ICIP, 2005, vol. 2, pp. 918–921.
31. 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.
32. T. Sim, S. Baker, and M. Bsat, “The CMU Pose, Illumination, and Expression (PIE) database of human faces,” in Proc. 5th IEEE Int. Conf. Autom. Face Gesture Recog., 2002, pp. 46–51.
33. W. S. Chen, P. C. Yuen, J. Huang, and D. Q. Dai, “Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 35, no. 4, pp. 659–669, Aug. 2005.