Noisy Iris Verification: A Modified Version of Local Intensity Variation Method

  • Authors:

  • Nima Tajbakhsh;Babak Nadjar Araabi;Hamid Soltanian-Zadeh

  • Affiliations:

  • Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran;Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran and School of Cognitive Sciences, IPM, Tehran, Iran;Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Iran and School of Cognitive Sciences, IPM, Tehran, Iran and Radiology ...

  • Venue:
  • ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
  • Year:
  • 2009

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Abstract

In this paper, a modified version of local intensity variation method is proposed to enhance the efficiency of identification system while dealing with degradation factors presented in iris texture. Our contributions to improve the robustness and performance of local intensity variation method consist of defining overlapped patches to compensate for deformation of texture, performing a de-noising strategy to remove high frequency components of intensity signals, proposing to add a coding strategy, and combining the dissimilarity values obtained from intensity signals. Experimental results on UBIRIS database demonstrate the effectiveness of proposed method when facing low quality images. To assess the robustness of proposed method to noise, lack of focus, and motion blur, we simulate these degradation factors that may occur during image acquisition in non-ideal conditions. Our results on a private database show that verification performance remains acceptable while the original method [11] suffers from a dramatic degradation.