Wavelet SIFT Feature Descriptors for Robust Face Recognition

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)


This paper presents a new robust face recognition technique based on the extraction and matching of Wavelet-SIFT features from individual face images. Here, Biorthogonal wavelet 4.4 is employed as the basis for Discrete Wavelet Transform of the images. Then, SIFT Face recognition method is applied on LL and HH sub band combination of images for recognition. The results obtained with the proposed method are compared with basic SIFT face recognition and classic appearance based face recognition technique (PCA) over three face databases: Nottingham database, Aberdeen database and Iranian database.


Scale Invariant Feature Transform (SIFT) Wavelet Transform Face Recognition Principal Component Analysis (PCA) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhang, B.-L., Zhang, H., Ge, S.S.: Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Transactions on Neural Networks 15(1), 166–177 (2004)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  3. 3.
    Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the Use of SIFT Features for Face Authentication. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, vol. 35, pp. 17–22 (June 2006)Google Scholar
  4. 4.
    Brown, M., Lowe, D.: Invariant Features from Interest Point Groups. Computer 332(6031), 253–262 (2002)Google Scholar
  5. 5.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Key-points. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  6. 6.
    Daubechies, I.: TenLectureonWavelets. CBMS, vol. 61. SIAM (1994)Google Scholar
  7. 7.
    Lucey, S., Matthews, I., Hu, C., Ambadar, Z., de la Torre, F., Cohn, J.: AAM derived face representations for robust facial action recognition. In: 7th International Conference on Automatic Face and Gesture Recognition, vol. 2-6, pp. 155–160 (April 2006)Google Scholar
  8. 8.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)Google Scholar
  9. 9.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  10. 10.
    Belhumeur, P.N., Hespanha, O.P., Kriegman, D.J.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  11. 11.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recogni-tion: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar
  12. 12.
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNITCalicutIndia

Personalised recommendations