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A novel retina-based human identification algorithm based on geometrical shape features using a hierarchical matching

Pouya Nazari1, Hossein Pourghassem2

  • 1Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran.; Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

Computer Methods and Programs in Biomedicine
|March 1, 2017
PubMed
Summary

This study introduces a novel retinal image identification algorithm using Surrounded Regions (SRs) and hierarchical matching. The algorithm achieves 100% accuracy, offering a secure and efficient biometric solution resilient to eye movements.

Keywords:
Decision making scenarioHierarchical matching structureRegion-based shape featureRetina-based human identificationSurrounded region

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Area of Science:

  • Biometrics
  • Computer Vision
  • Medical Imaging

Background:

  • Retinal images are highly secure biometrics for human identification.
  • Existing methods face challenges with rotation, translation, and natural eye movements.

Purpose of the Study:

  • To develop a rotation and translation-invariant human identification algorithm using retinal images.
  • To improve the accuracy and efficiency of biometric identification through novel feature extraction and matching.

Main Methods:

  • Representing retinal images using Surrounded Regions (SRs) defined by blood vessels.
  • Extracting novel region-based and boundary-based features from SRs, including corner angles and centroid distances.
  • Employing a hierarchical matching structure for efficient search space reduction and identification decision-making.

Main Results:

  • Achieved 100% accuracy on STARE and DRIVE retinal image databases.
  • Demonstrated high efficiency with average processing times around 3.2 seconds.
  • Validated robustness against eye movements during image acquisition.

Conclusions:

  • The proposed SR-based features and hierarchical matching significantly reduce computational complexity.
  • The algorithm enhances identification performance and overcomes challenges posed by natural head and eye movements.
  • This method offers a secure, accurate, and efficient biometric identification system.