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Ordinal measures for iris recognition.

Zhenan Sun1, Tieniu Tan

  • 1Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Haidian District, Beijing, PR China. znsun@nlpr.ia.ac.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|October 17, 2009
PubMed
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This study introduces ordinal measures for iris recognition, offering a robust and efficient method for identity authentication. These novel iris features provide a strong trade-off between distinctiveness and invariance to lighting conditions.

Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Iris recognition relies on textural information for identity authentication.
  • Representing iris texture with compact features remains a challenge.

Purpose of the Study:

  • To propose ordinal measures for iris feature representation.
  • To achieve a balance between distinctiveness and robustness in iris recognition.

Main Methods:

  • Utilizing ordinal measures to characterize qualitative relationships between iris regions.
  • Developing multilobe differential filters for ordinal measure computation.
  • Employing flexible parameters (location, scale, orientation, distance) for feature extraction.

Main Results:

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  • Ordinal measures are intrinsic and largely invariant to illumination changes.
  • The proposed method offers compactness and low computational complexity.
  • Experiments on public databases confirm the effectiveness of ordinal feature models.

Conclusions:

  • Ordinal measures provide an effective and efficient approach for iris recognition.
  • This method enhances robustness and distinctiveness for identity authentication.