Face Recognition under Variant Illumination Using PCA and Wavelets

  • Mong-Shu Lee
  • Mu-Yen Chen
  • Fu-Sen Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5575)


In this paper, an efficient wavelet subband representation method is proposed for face identification under varying illumination. In our presented method, prior to the traditional principal component analysis (PCA), we use wavelet transform to decompose the image into different frequency subbands, and a low-frequency subband with three secondary high-frequency subbands are used for PCA representations. Our aim is to compensate for the traditional wavelet-based methods by only selecting the most discriminating subband and neglecting the scattered characteristic of discriminating features. The proposed algorithm has been evaluated on the Yale Face Database B. Significant performance gains are attained.


Face recognition Principal component analysis Wavelet transform Illumination 


  1. 1.
    Adini, Y., Moses, Y., Ullman, S.: Face recognition: The problem of compensating for changes in illumination direction. IEEE Transaction on Pattern Analysis and Machine Intelligence 19, 721–732 (1997)CrossRefGoogle Scholar
  2. 2.
    Belhumeur, P.N.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transaction on Pattern Analysis and Machine Intelligence 19, 711 (1997)CrossRefGoogle Scholar
  3. 3.
    Chang, T., Kuo, C.: Texture analysis and classification with tree-structured wavelet transform. IEEE Tran. on Image Processing 2, 429 (1993)CrossRefGoogle Scholar
  4. 4.
    Chien, J.T., Wu, C.C.: Discriminant waveletfaces and nearest feature classifiers for face recognition. IEEE Transaction on Pattern Analysis and Machine Intelligence 24(12), 1644–1649 (2002)CrossRefGoogle Scholar
  5. 5.
    Daubechies, I.: Ten Lectures on Wavelets. In: SIAM. CBMB Regional Conference in Applied Mathematics Series, vol. 61 (1993)Google Scholar
  6. 6.
    DeVore, R., Jawerth, B., Lucier, B.: Image compression through wavelet transform coding. IEEE Trans. on Information Theory 38, 719–746 (1992)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Ekenel, H.K., Sanker, B.: Multiresolution face recognition. Image and Vision Computing (23), 469–477 (2005)CrossRefGoogle Scholar
  8. 8.
    Etemad, K., Chellappa, R.: Face recognition using Discreminant eigenvectors. In: Proceeding IEEE Int’l. Conf. Acoustic, Speech, and Signal Processing, pp. 2148–2151 (1996)Google Scholar
  9. 9.
    Feng, G.C., Yuen, P.C.: Human face recognition using PCA on wavelet subband. Journal of Eectronic Imaging (9), 226–233 (2000)CrossRefGoogle Scholar
  10. 10.
    Georghiades, A., Kriegman, D., Belhumeur, P.: Illumination cones for recognition under variable lighting: faces. In: Proceeding IEEE C CVPR SANT B (1998)Google Scholar
  11. 11.
    Lyons, M.J., Budynek, J., Akamatsu, S.: Automatic classification of single facial image. IEEE Transaction on Pattern Analysis and Machine Intelligence 21(12), 1357–1362 (1999)CrossRefGoogle Scholar
  12. 12.
    Moon, H., Phillips, J.: Analysis of PCA-based face recognition algorithms. In: Boyer, K., Phillips, J. (eds.) Empirical Evaluation Methods in Computer Vision. World Scientific Press, MD (1998)Google Scholar
  13. 13.
    Nastar, C., Ayach, N.: Frequency-based nongrid motion analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 18, 1067–1079 (1996)CrossRefGoogle Scholar
  14. 14.
    Shashua, A.: The quotient image: Class-based re-rendering and recognition with varying illuminations. IEEE Transaction on Pattern Analysis and Machine Intelligence 23(2), 129–139 (2001)CrossRefGoogle Scholar
  15. 15.
    Turk, M., Pentland, A.: EIigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71 (1991)CrossRefGoogle Scholar
  16. 16.
    Xie, X., Lam, K.: An efficient illumination normalization method for face recognition. Pattern Recognition Letters 27(6), 609–617 (2006)CrossRefGoogle Scholar
  17. 17.
    Yambor, W., Draper, B., Beveridge, R.: Analyzing PCA-based face recognition algorithms: eigenvector selection and distance measures. In: Christensen, H., Phillips, J. (eds.) Empirical Evaluation Methods in Computer Vision. World Scientific Press, Singapore (2002)Google Scholar
  18. 18.
    Zhao, J., Su, Y., Wang, D., Luo, S.: Illumination ratio image: synthesizing and recognition with varying illuminations. Pattern Recognition Letters (24) (2003)Google Scholar
  19. 19.
    Zhao, J., Chellappa, R.: Illumination-insensitive face recognition using symmetric shape-from-shading. In: Proceeding IEEE conf. CVPR Hilton Head (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mong-Shu Lee
    • 1
  • Mu-Yen Chen
    • 1
  • Fu-Sen Lin
    • 1
  1. 1.Department of Computer Science and EngineeringNational Taiwan Ocean UniversityKeelungTaiwan

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