By Topic

Iris Recognition Using Signal-Level Fusion of Frames From Video

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Karen Hollingsworth ; Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA ; Tanya Peters ; Kevin W. Bowyer ; Patrick J. Flynn

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, Krichen, and Schmid). 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 N 2 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.

Published in:

IEEE Transactions on Information Forensics and Security  (Volume:4 ,  Issue: 4 )