Efficient Iris Recognition Using Adaptive Quotient Thresholding

  • Peeranat Thoonsaengngam
  • Kittipol Horapong
  • Somying Thainimit
  • Vutipong Areekul
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


This paper presents an intensity-based iris recognition system. The system exploits local intensity changes of the visible iris textures such as crypts and naevi. The textures are extracted using local histogram equalization and the proposed ‘quotient thresholding’ technique. The quotient thresholding partitions iris images in a database such that a ratio between foreground and background of each image is retained. By fixing this ratio, variations of illumination across iris images are compensated, resulting in informative and distinctive blob-like iris textures. An agreement of the two extracted textures is measured by finding spatial correspondences between the textures. The proposed system yields the 0.22 %EER and 100%CRR. The experimental results indicate encouraging and effective iris recognition system, especially when it is used in identification mode. The system is very robust to changes in decision ratio.


Iris Image Iris Recognition Propose Feature Extraction Iris Recognition System Iris Texture 
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

  • Peeranat Thoonsaengngam
    • 1
  • Kittipol Horapong
    • 1
  • Somying Thainimit
    • 1
  • Vutipong Areekul
    • 1
  1. 1.Kasetsart Signal and Image Processing Laboratory (KSIP lab), Department of Electrical Engineering, Faculty of EngineeringKasetsart UniversityBangkokThailand

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