A Classifier Ensemble for Face Recognition Using Gabor Wavelet Features

  • Hamid Parvin
  • Nasser Mozayani
  • Akram Beigi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6694)


Gabor wavelet-based methods have been proven that are useful in many problems including face detection. It has been shown that these features tackle well facing into image recognition. In image identification, while there is a number of human faces in a repository of employees, it is aimed to identify the face of an arrived employee is which one? So the application of gabor wavelet-based features is reasonable. We propose a weighted majority average voting classifier ensemble to handle the problem. We show that the proposed mechanism works well in an employees’ repository of our laboratory.


Classifier Ensemble Gabor Wavelet Features Face Recognition Image Processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ross, A., Jain, A.K.: Information fusion in biometrics. Pattern Recognition Letters 24(13), 2115–2125 (2003)CrossRefGoogle Scholar
  2. 2.
    Pekalska, E., Duin, R., Skurichina, M.: A discussion on the classifier projection space for classifier combining. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 137–148. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Kittler. J., Li. Y.P., Matas. J., Sánchez, M.U.R.: Combining evidence in multimodal personal identity recognition systems. Audio- and Video-Based Biometric Authentication (1997)Google Scholar
  4. 4.
    Kittler, J., Matas, G., Jonsson, K., Sánchez, M.: Combining evidence in personal identity verification systems. Pattern Recognition Letters 18(9), 845–852 (1997)CrossRefGoogle Scholar
  5. 5.
    Bilmes, J., Kirchhoff, K.: Directed graphical models of classifier combination: Application to phone recognition. Spoken Language Processing (2000)Google Scholar
  6. 6.
    Tax, D.M.J., Breukelen, M.V., Duin, R.P.W., Kittler, J.: Combining multiple classifiers by averaging or by multiplying. Pattern Recognition 33, 1475–1485 (2000)CrossRefGoogle Scholar
  7. 7.
    Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. Wiley, New York (2005)zbMATHGoogle Scholar
  8. 8.
    Singh, S., Singh, M.: A dynamic classifier selection and combination approach to image region labeling. Signal Processing: Image Communication 20, 219–231 (2005)Google Scholar
  9. 9.
    Štruc, V., Pavešić, N.: Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition. INFORMATICA 20(1), 115–138 (2009)zbMATHGoogle Scholar
  10. 10.
    Hong, L., Jain, A., Pankanti, S., Bolle, R.: Fingerprint enhancement. In: IEEE WACV, Sarasota, pp. 202–207 (1996)Google Scholar
  11. 11.
    Bashar, M.K., Matsumoto, T., Ohnishi, N.: Wavelet transform-based locally orderless images for texture segmentation. Pattern Recognition Letters 24(15), 2633–2650 (2003)CrossRefGoogle Scholar
  12. 12.
    Žibert, J., Mihelič, F.: Zhao–Atlas–Marks representation of speech signals. Electrotechnical Review 69(3-4), 159–164 (2002)Google Scholar
  13. 13.
    Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hamid Parvin
    • 1
  • Nasser Mozayani
    • 1
  • Akram Beigi
    • 1
  1. 1.School of Computer EngineeringIran University of Science and Technology (IUST)TehranIran

Personalised recommendations