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Modeling Intra-class Variation for Nonideal Iris Recognition

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

Intra-class variation is fundamental to the FNMR performance of iris recognition systems. In this paper, we perform a systematic study of modeling intra-class variation for nonideal iris images captured under less-controlled environments. We present global geometric calibration techniques for compensating distortion associated with off-angle acquisition and local geometric calibration techniques for compensating distortion due to inaccurate segmentation or pupil dilation. Geometric calibration facilitates both the localization and recognition of iris and more importantly, it offers a new approach of trading FNMR with FMR. We use experimental results to demonstrate the effectiveness of the proposed calibration techniques on both ideal and non-ideal iris databases.

Keywords

Iris Image Iris Recognition Local Calibration Geometric Calibration Iris Localization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xin Li
    • 1
  1. 1.Lane Dept. of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantown

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