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Iris recognition approach for identity verification with DWT and multiclass SVM.

Mohamed A El-Sayed1,2, Mohammed A Abdel-Latif3

  • 1Technology Department, Applied College, Taif University, Taif, Saudi Arabia.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary

This study introduces an advanced iris recognition (IR) technique for secure identity verification. The method achieves high accuracy by efficiently extracting key iris features, enhancing biometric security.

Keywords:
Biometrics featureDWTDaugman modelDentitionHistogramHough transformIris datasetIris recognitionSVMVerification

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision
  • Machine Learning

Background:

  • Iris recognition (IR) is a highly accurate biometric modality used for identity authentication in secure environments.
  • Existing IR systems require robust pre-processing and feature extraction for reliable performance.
  • The need for efficient and accurate iris feature representation remains a key research area.

Purpose of the Study:

  • To propose a novel iris recognition technique for identity authentication and verification.
  • To develop an efficient method for iris image pre-processing, feature extraction, and matching.
  • To evaluate the performance and accuracy of the proposed iris recognition system.

Main Methods:

  • Iris image pre-processing: segmentation (Hough Transform), normalization (Daugman's rubber sheet model), and enhancement (histogram equalization).
  • Feature extraction using Gabor wavelets and Discrete Wavelets Transform.
  • Image matching and classification using a multiclass Support Vector Machine (SVM).

Main Results:

  • The proposed method significantly reduces iris features to 88 per image.
  • The system demonstrated effective extraction of prominent iris characteristics.
  • Achieved a high recognition accuracy of 98.92% on the IITD iris dataset.

Conclusions:

  • The developed iris recognition technique offers an efficient and accurate solution for identity verification.
  • The method's feature reduction and high accuracy make it suitable for real-world biometric applications.
  • This approach advances the field of iris recognition with its performance and reduced feature set.