Human Face Recognition with Different Statistical Features

  • Javad Haddadnia
  • Majid Ahmadi
  • Karim Faez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


This paper examines application of various feature domains for recognition of human face images to introduce an efficient feature extraction method. The proposed feature extraction method comprised of two steps. In the first step, a human face localization technique with defining a new parameter to eliminate the effect of irrelevant data is applied to the facial images. In the next step three different feature domains are applied to localized faces to generate the feature vector. These include Pseudo Zernike Moments (PZM), Principle Component Analysis (PCA) and Discrete Cosine Transform (DCT). We have compared the effectiveness of each of the above feature domains through the proposed feature extraction for human face recognition. The Radial Basis Function (RBF) neural network has been utilized as classifier. Simulation results on the ORL database indicate the effectiveness of the proposed feature extraction with the PZM for human face recognition.


Radial Basis Function Face Recognition Discrete Cosine Transform Face Image Principle Component Analysis 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Javad Haddadnia
    • 1
  • Majid Ahmadi
    • 1
  • Karim Faez
    • 2
  1. 1.Electrical and Computer Engineering DepartmentUniversity of WindsorWindsorCanada
  2. 2.Electrical Engineering DepartmentAmirkabir University of TechnologyTehranIran

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