Robust Face Recognition Based on Part-Based Localized Basis Images

  • Jongsun Kim
  • Juneho Yi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


In order for a subspace projection based method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method using ICA architecture I basis images that is robust to local distortion and partial occlusion. The proposed representation only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of “recognition by parts.” We have contrasted our representation with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that our representation performs better than PCA, ICA architecture I, ICA architecture II, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.


Face Recognition Independent Component Analysis Basis Image Partial Occlusion Local Distortion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Turk, M.A., Pentland, A.P.: Eigenfaces for recognition. Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face Recognition by Independent Component Analysis. IEEE Trans. Neural Networks 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  3. 3.
    Hyvarinen, A., Oja, E.: Independent component analysis: a tutorial (1999),
  4. 4.
    Hyvärinen, A.: The Fixed-point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis. Neural Processing Letters 10, 1–5 (1999)CrossRefGoogle Scholar
  5. 5.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE PAMI 19(7), 711–720 (1999)Google Scholar
  6. 6.
    Penev, P., Atick, J.: Local Feature Analysis: A general statistical theory for object representation. Network: Computation in Neural Systems 7(3), 477–500 (1996)zbMATHCrossRefGoogle Scholar
  7. 7.
    Draper, B.A., Baek, K., Bartlett, M.S., Beveridge, J.R.: Recognizing faces with PCA and ICA. Computer Vision and Image Understanding 91(1), 115–137 (2003)CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Martinez, A.M., Benavente, R.: The AR face database. CVC Tech (1998)Google Scholar
  10. 10.
    Pentland, A.P.: Recognition by parts. In: IEEE Proceedings of the First International Conference on Computer Vision, pp. 612–620 (1987)Google Scholar
  11. 11.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)CrossRefGoogle Scholar
  12. 12.
    Bell, A.J., Sejnowski, T.J.: The Independent Components of Natural Scenes are Edge Filters. Vision Research 37(23), 3327–3338 (1997)CrossRefGoogle Scholar
  13. 13.
    Li, S.Z., Hou, X.W., Zhang, H.J.: Learning Spatially Localized, Parts-Based Representation. Computer Vision and Pattern Recognition 1, 207–212 (2001)Google Scholar
  14. 14.
    Wild, S., Curry, J., Dougherty, A.: Motivating Non-Negative Matrix Factorizations. In: Proceedings of the Eighth SIAM Conference on Applied Linear Algebra (July 2003)Google Scholar
  15. 15.
    Bartlett, M.S.: Face Image Analysis by Unsupervised Learning. Kluwer Academic Publishers, Dordrecht (2001)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jongsun Kim
    • 1
  • Juneho Yi
    • 1
  1. 1.School of Information & Communication EngineeringSungkyunkwan University, Korea, Biometrics Engineering Research Center 

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