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. © Springer-Verlag Berlin Heidelberg 2009.
CITATION STYLE
Hollingsworth, K. P., Bowyer, K. W., & Flynn, P. J. (2009). Image averaging for improved iris recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5558 LNCS, pp. 1112–1121). https://doi.org/10.1007/978-3-642-01793-3_112
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