CFP last date
20 December 2024
Reseach Article

Article:Design and Implementation of Robust 2D Face Recognition System for Illumination Variations

by Kiran P.Gaikwad, V.M.Wadhai, Prasad S.Halgaonkar, Santosh Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 3
Year of Publication: 2010
Authors: Kiran P.Gaikwad, V.M.Wadhai, Prasad S.Halgaonkar, Santosh Kumar
10.5120/1458-1972

Kiran P.Gaikwad, V.M.Wadhai, Prasad S.Halgaonkar, Santosh Kumar . Article:Design and Implementation of Robust 2D Face Recognition System for Illumination Variations. International Journal of Computer Applications. 10, 3 ( November 2010), 39-43. DOI=10.5120/1458-1972

@article{ 10.5120/1458-1972,
author = { Kiran P.Gaikwad, V.M.Wadhai, Prasad S.Halgaonkar, Santosh Kumar },
title = { Article:Design and Implementation of Robust 2D Face Recognition System for Illumination Variations },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 3 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number3/1458-1972/ },
doi = { 10.5120/1458-1972 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:49.054165+05:30
%A Kiran P.Gaikwad
%A V.M.Wadhai
%A Prasad S.Halgaonkar
%A Santosh Kumar
%T Article:Design and Implementation of Robust 2D Face Recognition System for Illumination Variations
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 3
%P 39-43
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Illumination variation is a challenging problem in face recognition research area. Same person can appear greatly different under varying lighting conditions. This paper consists of Face Recognition System which is invariant to illumination variations. Face recognition system which uses Linear Discriminant Analysis (LDA) as feature extractor have Small Sample Size (SSS). It consists of implementation of Feature Extraction Module using Two Dimensional Maximum Margin Criteria which removes “Small Sample Size (SSS)” problem present in existing Face Recognition System.

References
  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. D. Zhang and Z. H. Zhou, “(2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition,” Neuro-computing, vol. 69, no. 1–3, pp. 224–231, Dec. 2005.
  24. S. Noushath, G. Hemantha Kumar, and P. Shivakumara, “(2D)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. http://www.face-rec.org
Index Terms

Computer Science
Information Sciences

Keywords

Robust 2D Face Illumination Variations