Iris recognition using signal-level fusion of frames from video

  • Authors:

  • Karen Hollingsworth;Tanya Peters;Kevin W. Bowyer;Patrick J. Flynn

  • Affiliations:

  • Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN;Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN

  • Venue:
  • IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
  • Year:
  • 2009

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Abstract

We take advantage of the temporal continuity in an iris video to improve matching performance using signal-level fusion. From multiple frames of a frontal iris video, we create a single average image. For comparison, we reimplement three score-level fusion methods (Ma et al., Krichen et al., and Schmid et al.). We find that our signal-level fusion of N images performs better than Ma's or Krichen's score-level fusion methods of N Hamming distance scores. Our signal-level fusion performs comparably to Schmid's log-likelihood method of score-level fusion, and our method achieves this performance using less computation time. We compare our signal fusion method with another new method: a multigallery, multiprobe method involving score-level fusion of N2 Hamming distances. The multigallery, multiprobe score fusion has slightly better recognition performance, while the signal fusion has significant advantages in memory and computation requirements. No published prior work has shown any advantage of the use of video over still images in iris biometrics.