Efficient Iris Spoof Detection via Boosted Local Binary Patterns

  • Zhaofeng He
  • Zhenan Sun
  • Tieniu Tan
  • Zhuoshi Wei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Recently, spoof detection has become an important and challenging topic in iris recognition. Based on the textural differences between the counterfeit iris images and the live iris images, we propose an efficient method to tackle this problem. Firstly, the normalized iris image is divided into sub-regions according to the properties of iris textures. Local binary patterns (LBP) are then adopted for texture representation of each sub-region. Finally, Adaboost learning is performed to select the most discriminative LBP features for spoof detection. In particular, a kernel density estimation scheme is proposed to complement the insufficiency of counterfeit iris images during Adaboost training. The comparison experiments indicate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zhaofeng He
    • 1
  • Zhenan Sun
    • 1
  • Tieniu Tan
    • 1
  • Zhuoshi Wei
    • 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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