Optimal Sampling for Feature Extraction in Iris Recognition Systems

  • Luis E. Garza Castañon
  • Saul Montes de Oca
  • Rubén Morales-Menéndez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


Iris recognition is a method used to identify people based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: (1) image acquisition and preprocessing, (2) iris localization and extraction, (3) iris features characterization, and (4) comparison and matching. A novel contribution in the step of characterization of iris features is introduced by using a Hammersley’s sampling algorithm and accumulated histograms. Histograms are computed with data coming from sampled sub-images of iris. The optimal number and dimensions of samples is obtained by the simulated annealing algorithm. For the last step, couples of accumulated histograms iris samples are compared and a decision of acceptance is taken based on an experimental threshold. We tested our ideas with UBIRIS database; for clean eye iris databases we got excellent results.


Simulated Annealing Algorithm Iris Image Texture Synthesis Iris Feature Iris Recognition 
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

  • Luis E. Garza Castañon
    • 1
  • Saul Montes de Oca
    • 2
  • Rubén Morales-Menéndez
    • 3
  1. 1.Dept. of Mechatronics and Automation
  2. 2.Automation Graduate Program Student
  3. 3.Center for Innovation in Design and TechnologyMonterreyMéxico

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