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Multidimensional Systems and Signal Processing

, Volume 21, Issue 3, pp 213–229 | Cite as

Perfect histogram matching PCA for face recognition

  • Ana-Maria Sevcenco
  • Wu-Sheng Lu
Article

Abstract

We present an enhanced principal component analysis (PCA) algorithm for improving rate of face recognition. The proposed pre-processing method, termed as perfect histogram matching, modifies the image histogram to match a Gaussian shaped tonal distribution in the face images such that spatially the entire set of face images presents similar facial gray-level intensities while the face content in the frequency domain remains mostly unaltered. Computationally inexpensive, the perfect histogram matching algorithm proves to yield superior results when applied as a pre-processing module prior to the conventional PCA algorithm for face recognition. Experimental results are presented to demonstrate effectiveness of the technique.

Keywords

Principal component analysis Histogram matching Face recognition 

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References

  1. Bartlett M. S., Movellan J. R., Sejnowski T. J. (2002) Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6): 1450–1464CrossRefGoogle Scholar
  2. Belhumeur P.N., Hespanha J., Kriegman D. (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on PAMI, Special Issue on Face Recognition 17(7): 711–720Google Scholar
  3. Belhumeur, P., & Kriegman, D. J. (1996). What is the set of images of an object under all possible lighting conditions? In Proceedings, IEEE conference on CVPR (pp. 207–277).Google Scholar
  4. Belkin M., Niyogi P. (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Advances in Neural Information Processing Systems 14: 585–591Google Scholar
  5. Belkin M., Niyogi P. (2008) Towards a theoretical foundation for Laplacian-based manifold methods. Journal of Computer and System Sciences 74(8): 1289–1308MATHCrossRefMathSciNetGoogle Scholar
  6. Blanz B., Vetter T. (2003) Face recognition based on fitting a 3D morphable model. IEEE Transactions on PAMI 25(9): 1063–1074Google Scholar
  7. Busch D. D. (2005) Mastering digital SLR photography. Thomson Course Technology, BostonGoogle Scholar
  8. Chichizola, F., De Giusti, L., De Giusti, A., & Naiouf, M. (2005). Face recognition: reduced image eigenfaces method. In ELMAR 47th International Symposium (pp. 159–162).Google Scholar
  9. Etemad, K., & Chellappa, R. (1996). Face recognition using discriminant eigenvectors. In Proceedings, IEEE ICASSP, 4 (pp. 2148–2151).Google Scholar
  10. Georghiades A. S., Belhumeur P. N., Kriegman D. J. (2001) From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on PAMI 23(6): 643–660Google Scholar
  11. Gonzalez R. C., Woods R. E. (2002) Digital image processing. Prentice-Hall, New JerseyGoogle Scholar
  12. Hallinan, P. (1994). A low-dimensional representation of human faces for arbitrary lighting conditions. In Proceedings, IEEE CVPR (pp. 995–999).Google Scholar
  13. He X., Yan S., Hu Y., Niyogi P., Zhang H.-J. (2005) Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3): 328–340CrossRefGoogle Scholar
  14. Hsieh P.-C., Tung P.-C. (2009) A novel hybrid approach based on sub-pattern technique and whitened PCA for face recognition. Pattern Recognition 42(5): 978–984MATHCrossRefGoogle Scholar
  15. Jain A. K. (1989) Fundamentals of digital image processing. Prentice Hall, New JerseyMATHGoogle Scholar
  16. Kanade, T., & Yamada, A. (2003). Multi-subregion based probabilistic approach toward pose-invariant face recognition. In Proceedings, IEEE computational intelligence in robotics automation (Vol. 2, pp. 954–959) Kobe, Japan.Google Scholar
  17. Kwak K.-C., Pedrycz W. (2007) Face recognition using an enhanced independent component analysis approach. IEEE Transactions on Neural Networks 18(2): 530–541CrossRefGoogle Scholar
  18. Langford M., Bilissi E. (2005) Langford’s advanced photography. Focal Press, ElsevierGoogle Scholar
  19. Lee, H.-S., & Kim, D. (2004). Pose invariant face recognition using linear pose transformation in feature space, In Proceedings, ECCV workshop computer vision in human-computer interaction, Czech Republic.Google Scholar
  20. Lee K. C., Ho J., Kriegman D. (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on PAMI 27(5): 684–698Google Scholar
  21. Liao L.-Z., Luo S.-W., Tian M. (2007) “Whitenedfaces” recognition with PCA and ICA. IEEE Signal Processing Letters 14(12): 1008–1011CrossRefGoogle Scholar
  22. Liu, X., & Chen, T. (2003). Video-based face recognition using adaptive hidden Markov models. In Proceedings, IEEE CVPR, 1 (pp. 340–345).Google Scholar
  23. Liu, X., & Chen, T. (2005). Pose-robust face recognition using geometry assisted probabilistic modeling. In Proceedings, IEEE CVPR, 1 (pp. 502–509).Google Scholar
  24. Li, Y., Gong, S., & Liddell, H. (2000). Recognizing the dynamics of faces across multiple views. In Proceedings, British machine vision conference (pp. 242–251). Bristol, England.Google Scholar
  25. Niu B., Yang Q., Shiu S. C. K., Pal S. K. (2008) Two-dimensional Laplacianfaces method for face recognition. Pattern Recognition 41(10): 3237–3243MATHCrossRefGoogle Scholar
  26. Okada, K., & von der Malsburg, C. (2002). Pose-invariant face recognition with parametric linear subspaces. In Proceedings, 5th international conference on automatic face and gesture recofnition (pp. 64–69). Washington D.C.Google Scholar
  27. Qing X., & Wang X. (2006). Face recognition using Laplacian+OPRA-faces. 6th World Congress on Intelligent Control and Automation, 2, 10013–10016.Google Scholar
  28. Ramasubramanian D., Venkatesh Y. V. (2001) Encoding and recognition of faces based on the human visual model and DCT. Pattern Recognition 34(12): 2447–2458MATHCrossRefGoogle Scholar
  29. Roweis S. T., Saul L. K. (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500): 2323–2326CrossRefGoogle Scholar
  30. Saul L. K., Roweis S. T. (2003) Think globally, fit locally: Unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4: 119–155CrossRefMathSciNetGoogle Scholar
  31. Shashua A. (1997) On photometric issues in 3D visual recognition from a single 2D image. International Journal of Computer Vision 21(1/2): 99–122CrossRefGoogle Scholar
  32. Tenenbaum J. B., Silva V., Langford J. C. (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500): 2319–2323CrossRefGoogle Scholar
  33. Turk, M. A., & Pentland, A. P. (1991). Face recognition using eigenfaces. In Proceedings, IEEE computer society conference on computer vision and pattern recognition (pp. 586–591).Google Scholar
  34. Yale Face Database, CT, USA: Yale University (1997). http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
  35. Yang J., Zhang D., Frangi A. F., Yang J.-Y. (2004) Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(1): 131–137CrossRefGoogle Scholar
  36. Zhao L., Yang Y.-H. (1999) Theoretical analysis of illumination in PCA-based vision systems. Pattern Recognition 34(4): 547–564CrossRefGoogle Scholar
  37. Zhao, W., Chellapa, R., Rosenfeld, A., & Phillips, P. J. (2003). Face recognition: A literature survey. ACM Computing Surveys, 399–458.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of VictoriaVictoriaCanada

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