Face Recognition: An Optimized Localization Approach and Selected PZMI Feature Vector Using SVM Classifier

  • Hamidreza Rashidy Kanan
  • Karim Faez
  • Mehdi Ezoji
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


In this paper a system is developed for face recognition processes. After preprocessing of face images, for omitting the redundant information such as background and hair, the oval shape of face is approximated by an ellipse using shape information. Then the parameters (orientation and center coordinates) of this ellipse are optimized using Genetic Algorithm (GA). High order Pseudo Zernike Moment Invariant (PZMI) which has useful properties is utilized to produce feature vectors. We use GAs in combination with nearest neighbor classifier to select the optimal feature set for classification. Also, Support Vector Machines (SVMs) which has very good generalization ability has been used as a classifier with ERBF kernel function. Proposed approach has been applied on ORL and Yale databases and has shown a high classification rate with small number of feature elements.


Face Recognition Face Image Genetic Algorithm Parameter Face Recognition System Face Localization 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chellappa, R., Wilson, C.L., Sirohry, S.: Human and Machine Recognition of faces: A Survey. Proceedings of the IEEE 83, 705–740 (1995)CrossRefGoogle Scholar
  2. 2.
    Kanan, H.R., Faez, K.: ZMI and Wavelet Transform Features and SVM Classifier in the Optimized Face Recognition system. In: Proceeding of the 5th IEEE International Symposium on Signal Processing and Information Technology, pp. 295–300 (2005)Google Scholar
  3. 3.
    Srinivas, M., Patnik, L.M.: Genetic Algorithms: A Survey. IEEE, Los Alamitos (1994)Google Scholar
  4. 4.
    Teh, C.H., Chin, R.T.: On Image Analysis by the Methods of Moments. IEEE Trans. On Patt. Anal. and Mach. Intell. 10, 496–513 (1988)MATHCrossRefGoogle Scholar
  5. 5.
    Osuna, E., Freund, R., Girosi, F.: Support Vector Machine: Training and Applications. Technical Report Massachusetts Institute of Technology (1997)Google Scholar
  6. 6.
    Daugman, J.: Face Detection: a Survey. Comput. Vis. Imag. Underst. 83, 236–274 (2001)CrossRefGoogle Scholar
  7. 7.
    Sobotta, K., Pitas, I.: Face Localization and Facial Feature Extraction Based on Shape and Color Information. In: IEEE Int. Conf. on Image Processing, vol. 3, pp. 483–486 (1996)Google Scholar
  8. 8.
    Lin, C.H., Wu, J.L.: Automatic Facial Feature Extraction by Genetic Algorithms. IEEE Transaction of Image Processing 8, 834–845 (1999)CrossRefGoogle Scholar
  9. 9.
    Kanan, H.R., Faez, K., Mozaffari, S., Ezoji, M.: Face Localization Using Shape Information and Genetic Algorithm. In: Proceeding of the First Int. Conference on Modeling, Simulation and Applied Optimization (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hamidreza Rashidy Kanan
    • 1
  • Karim Faez
    • 1
  • Mehdi Ezoji
    • 1
  1. 1.Image Processing and Pattern Recognition Lab., Electrical Engineering DepartmentAmirKabir University of Technology (Tehran Polytechnic)TehranIran

Personalised recommendations