Image Averaging for Improved Iris Recognition

  • Karen P. Hollingsworth
  • Kevin W. Bowyer
  • Patrick J. Flynn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


We take advantage of the temporal continuity in an iris video to improve matching performance using signal-level fusion. From multiple frames of an iris video, we create a single average image. Our signal-level fusion method performs better than methods based on single still images, and better than previously published multi-gallery score-fusion methods. We compare our signal fusion method with another new method: a multi-gallery, multi-probe score fusion method. Between these two new methods, the multi-gallery, multi-probe score fusion has slightly better recognition performance, while the signal fusion has significant advantages in memory and computation requirements.


Average Image Iris Recognition Gallery Image Signal Fusion Good Recognition Performance 
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 2009

Authors and Affiliations

  • Karen P. Hollingsworth
    • 1
  • Kevin W. Bowyer
    • 1
  • Patrick J. Flynn
    • 1
  1. 1.University of Notre DameUSA

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