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Robust security using code level approach for iris recognition

  • Unique Paper ID: 146582
  • Volume: 4
  • Issue: 12
  • PageNo: 942-948
  • Abstract:
  • Matching heterogeneous iris images in less con-strained applications of iris biometrics is becoming a challenging task. The existing solutions try to reduce the difference between heterogeneous iris images in pixel intensities or filtered features. In contrast, this paper proposes a code-level approach in het-erogenous iris recognition. The non-linear relationship between binary feature codes of heterogeneous iris images is modeled by an adapted Markov network. This model transforms the number of iris templates in the probe into a homogenous iris template corresponding to the gallery sample. In addition, a weight map on the reliability of binary codes in the iris template can be derived from the model. The learnt iris template and weight map are jointly used in building a robust iris matcher against the variations of imaging sensors, capturing distance and subject conditions. Extensive experimental results of matching cross-sensor, high-resolution vs low-resolution and, clear vs blurred iris images demonstrate the code-level approach can achieve the highest accuracy in compared to the existing pixel-level, feature-level and score-level solutions
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 4
  • Issue: 12
  • PageNo: 942-948

Robust security using code level approach for iris recognition

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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