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Gorazd Vrček, Peter PeerComputer Vision Laboratory
Faculty of Computer and Information Science, University of Ljubljana Ljubljana, Slovenia
Chalkida, June 19 2009
IWSSIP 2009
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Verification, biometry, iris?
System architecture
Results
Conclusion
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Iris
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Segmentation
Normalization
Feature extraction
Iris comparison
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Input image? ROI? Problems (noise)? Segmentation goal? Start...
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Getting information about the pupil: Pupil edge
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Getting information about the pupil: Center
Radius
(1) indexXleft(2) Xz
(3) coarse center(4) indexYbottom(5) indexXright(6) Cz
(7) indexYup(8) Yz
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Getting information about the pupil (outer edge):
Image smoothing Image illumination
Outer iris edge points detection Generating iris mask
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Based on Dougman’s homogeneous rubber sheet
With the center in the center of the pupil
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Gabor filter (2D Gabor wavelet)
Image convolution with it
The phase transformation used to convert the angles into iris template
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Comparison of two iris bit templates Considering iris mask
Shift the bits and calculate again Use the minimal Hamming distance
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The comparison within the class provides the comparison of seven images of a person among themselves
The comparison between classes provides the comparison of one iris image of a person with one of all other persons
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Result: positive/negative Threshold for positive decision is set to
HD≤0.427
value 0.427 gives FAR 0%, FRR 11.584%
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Research prototype → good results Comparison with ICE 2006 results (FAR=0.1%):
To improve: segmentation optimization, noise detection
To upgrade: integrate iris capturing sensor
Group FRR [%]Sagem-Iridian 2.31Cambridge 3.29Iritech 3.84
CVL 7.70
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