A Passport Recognition and Face Verification Using Enhanced Fuzzy Neural Network and PCA Algorithm

  • Kwang-Baek Kim
  • Sungshin Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


In this paper, passport recognition and face verification methods which can automatically recognize passport codes and discriminate forgery passports to improve efficiency and systematic control of immigration management are proposed. Adjusting the slant is very important for recognition of characters and face verification since slanted passport images can bring various unwanted effects to the recognition of individual codes and faces. The angle adjustment can be conducted by using the slant of the straight and horizontal line that connects the center of thickness between left and right parts of the string. Extracting passport codes is done by Sobel operator, horizontal smearing, and 8-neighbornood contour tracking algorithm. The proposed RBF network is applied to the middle layer of RBF network by using the fuzzy logic connection operator and proposing the enhanced fuzzy ART algorithm that dynamically controls the vigilance parameter. After several tests using a forged passport and the passport with slanted images, the proposed method was proven to be effective in recognizing passport codes and verifying facial images.


Facial Image Radial Basis Function Neural Network Sobel Operator Individual Code Forgery Detection 
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 2006

Authors and Affiliations

  • Kwang-Baek Kim
    • 1
  • Sungshin Kim
    • 2
  1. 1.Department of Computer Eng.Silla UniversityBusanKorea
  2. 2.School of Electrical and Computer Eng.Pusan National UniversityBusanKorea

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