Global Texture Analysis of Iris Images for Ethnic Classification

  • Xianchao Qiu
  • Zhenan Sun
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Iris pattern is commonly regarded as a kind of phenotypic feature without relation to the genes. In this paper, we propose a novel ethnic classification method based on the global texture information of iris images. So we would argue that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In our scheme, a bank of multichannel 2D Gabor filters is used to capture the global texture information and AdaBoost is used to learn a discriminant classification principle from the pool of the candidate feature set. Finally iris images are grouped into two race categories, Asian and non-Asian. Based on the proposed method, we get an encouraging correct classification rate (CCR) of 85.95% on a mixed database containing 3982 iris samples in our experiments.


Face Image Iris Image CASIA Database Global Texture Iris Database 
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 2005

Authors and Affiliations

  • Xianchao Qiu
    • 1
  • Zhenan Sun
    • 1
  • Tieniu Tan
    • 1
  1. 1.Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingP.R. China

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