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• W2144070539 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, Krichen, and Schmid). We find that our signal-level fusion of <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>N</i> images performs better than Ma's or Krichen's score-level fusion methods of <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>N</i> 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 <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>N</i> <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> 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." @default.
• W2144070539 created "2016-06-24" @default.
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• W2144070539 date "2009-12-01" @default.
• W2144070539 modified "2023-09-24" @default.
• W2144070539 title "Iris Recognition Using Signal-Level Fusion of Frames From Video" @default.
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• W2144070539 doi "https://doi.org/10.1109/tifs.2009.2033759" @default.
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