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Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata.

Afshin Ghanizadeh1, Amir Atapour Abarghouei, Saman Sinaie

  • 1Soft Computing Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. afshin.ghanizadeh@gmail.com

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This study presents an improved iris segmentation method using edge detection and Hough transforms for more efficient and accurate iris recognition systems. The new technique enhances overall system performance compared to conventional approaches.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Iris recognition systems offer unique and long-lasting individual identification.
  • Accurate iris segmentation is crucial for the performance of biometric systems.
  • Preprocessing, including iris segmentation, is a critical phase in iris recognition.

Purpose of the Study:

  • To propose a novel iris segmentation system for enhanced accuracy and efficiency.
  • To improve the performance of iris biometric systems through advanced segmentation techniques.

Main Methods:

  • The study employs edge detection techniques for iris segmentation.
  • Hough transforms are utilized in conjunction with edge detection.
  • A new edge detection system is developed to enhance segmentation.

Main Results:

  • The proposed edge detection system significantly improves segmentation performance.
  • The developed system is more efficient than conventional iris segmentation methods.
  • Enhanced segmentation accuracy contributes to better iris recognition.

Conclusions:

  • The novel iris segmentation system using edge detection and Hough transforms is highly effective.
  • This method offers a more efficient alternative to existing iris segmentation techniques.
  • The improved segmentation accuracy is vital for reliable iris-based biometrics.