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A Novel Technique For Human Face Recognition Using Fractal Code and Bi-dimensional Subspace

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Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 456)


Face recognition is considered as one of the best biometric methods used for human identification and verification; this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for face recognition and classification using a system based on WPD, fractal codes and two-dimensional subspace for feature extraction, and Combined Learning Vector Quantization and PNN Classifier as Neural Network approach for classification. This paper presents a new approach for extracted features and face recognition .Fractal codes which are determined by a fractal encoding method are used as feature in this system. Fractal image compression is a relatively recent technique based on the representation of an image by a contractive transform for which the fixed point is close to the original image. Each fractal code consists of five parameters such as corresponding domain coordinates for each range block. Brightness offset and an affine transformation. The proposed approach is tested on ORL and FEI face databases. Experimental results on this database demonstrated the effectiveness of the proposed approach for face recognition with high accuracy compared with previous methods.


  • Biometric
  • Face recognition
  • 2DPCA
  • 2DLDA
  • DWT
  • PNN
  • WPD
  • IFS
  • Fractal codes
  • LVQ


  1. Jain, A.K. (ed.).: Handbook of Biometrics. Michigan State University, USA Patrick Flynn University of Notre Dame, USA Arun A. Ross West Virginia University, USA © Springer Science+Business Media, LLC (2008)

    Google Scholar 

  2. Pato, J.N., Millett, L.I. (eds.).: Biometric recognition challenges and opportunities Whither Biometrics Committee Computer Science and Telecommunications Board Division on Engineering and Physical Sciences Copyright by the National Academy of Sciences (2010)

    Google Scholar 

  3. Zhang, D., Zhou, Z.-H. (2D) 2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition. Neuro Computing 69, 224–231 (2005)

    Google Scholar 

  4. Nguyen, N., Liu, W., Venkatesh, S.: Random Subspace Two-Dimensional PCA for Face Recognition. Department of Computing, Curtin University of Technology, WA 6845, Australia

    Google Scholar 

  5. Yang, J., Zhang, D.: Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition  26(1) (January 2004)

    Google Scholar 

  6. Noushath, S., Kumar, G.H., Shivakumara, P. (2D)LDA: An efficient approach for face recognition. Pattern Recognition 39(7), 1396–1400 (2006)

    CrossRef  MATH  Google Scholar 

  7. Mallat, S.: A theory of multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)

    CrossRef  MATH  Google Scholar 

  8. Feng, G.C., Yuen, P.C., Dai, D.Q.: Human face recognition using PCA on wavelet subband. SPIE Journal of Electronic Imaging 9(2), 226–233 (2000)

    CrossRef  Google Scholar 

  9. Barnsley, M.: Fractals Everywhere. Academic Press, San Diego (1988)

    MATH  Google Scholar 

  10. Jacquin, A.E.: Fractal image coding: A review. Proc. of the IEEE 81, 1451–1465 (1993)

    CrossRef  Google Scholar 

  11. Jacquin, A.E.: A Fractal Theory of Iterated Markov Operators with Applications to Digital

    Google Scholar 

  12. Image Coding, PhD thesis, Georgia Tech, 1989. Y. Fisher, Fractal Image Compression: Theory and Application, Springer-Verlag Inc. (1995)

    Google Scholar 

  13. Ebrahimpour-Komleh, H.: Face recognition using fractal codes. In: Proceedings of International Conference on Image Processing 2001. IEEE, Thessaloniki (2001)

    Google Scholar 

  14. Nazish.: Face recognition using neural networks. Proc. IEEE INMIC 2001, 277–281 (2007)

    Google Scholar 

  15. Specht, D.F.: Probabilistic neural network and the polynomial adaline as complementary techniques for classification. IEEE Trans. Neural Networks 1(1), 111–121 (1990)

    CrossRef  Google Scholar 

  16. Neural network toolbox matlabUser’s Guide COPYRIGHT 1992 - 2002 by The MathWorks, Inc.

    Google Scholar 

  17. Computational intelligence paradigms: theory & applications using MATLAB / S. Sumathi and Surekha Paneerselvam. 2010 by Taylor and Francis Group

    Google Scholar 

  18. ORL. The ORL face database at the AT&T (Olivetti) Research Laboratory (1992)

    Google Scholar 

  19. FEI. The FEI face database at the Artificial Intelligence Laboratory of FEI in São Bernardo do Campo, São Paulo, Brazil (June 2005/March 2006)

    Google Scholar 

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Mohamed, B., Kamel, B.M., Redwan, T., Mohamed, S. (2015). A Novel Technique For Human Face Recognition Using Fractal Code and Bi-dimensional Subspace. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham.

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