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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Iris recognition using image moments and k-means algorithm.

Yaser Daanial Khan1, Sher Afzal Khan2, Farooq Ahmad3

  • 1School of Science and Technology, University of Management and Technology, Lahore 54000, Pakistan ; Department of Computer Science, AbdulWali Khan University, Mardan 23200, Pakistan.

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Summary

This study introduces an accurate biometric identification method using iris images. The technique achieves 98.5% accuracy by analyzing unique iris patterns for reliable person identification.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Iris recognition is a key biometric technology for secure identification.
  • Existing methods face challenges with variations in scale, rotation, and translation.

Purpose of the Study:

  • To develop a robust iris-based biometric identification system.
  • To achieve high accuracy in person identification using iris image features.

Main Methods:

  • Iris segmentation using edge detection algorithms.
  • Transformation of the iris region into a rectangular format.
  • Extraction of scale, rotation, and translation invariant moments for feature vectors.
  • K-means clustering for image classification.
  • Euclidean distance for centroid-based matching.

Main Results:

  • The proposed method successfully extracts invariant moments from iris images.
  • K-means clustering effectively groups similar iris patterns.
  • The system demonstrates a high accuracy rate of 98.5% in identification tasks.

Conclusions:

  • The developed iris recognition technique is highly accurate and robust.
  • Invariant moments and k-means clustering provide an effective feature extraction and classification strategy for biometrics.