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J. Daugman, “How iris recognition works”, Proceedings of the International Conference on Image Processing, 22-25 September 2002
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Our approach : Packet method
• Process the whole image at each level of resolution
• Starting with higher mother wavelet window
• 1664 coefficients for coding iris
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Databases • IrisINT : Iris images recorded under
normal light illumination. 70 persons 700 images.
• CASIA : Iris images taken under infra red illumination. 110 persons, 770 images. Recorded at NLPR China.
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Roc curves (IrisINT)
•Poor results for the wavelet method
•The wavelet Packet method is more robust using visible light images
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Comparative results on CASIA and IrisINT
Databases IrisINT CASIA
Type of errors FAR FRR FAR FRR
Classical wavelet method 2% 12.04% 0.35% 2.08%
Packets method 0% 0.57% 0.2% 1.38%
• With infra red illumination, the two methods have quite the same performance. WP is more robust to the presence of eyelids or eyelashes.
C.P. Strouthopoulos, Adaptive color reduction
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Use of color information
ACR method
Original color image(71.000 different colors)
Color image (256 colors)
We perform iris recognition using the same algorithm as the one developed for grey level image
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Use of color information :ROC curve on IrisINT
Use of color information allows a better discrimination between the persons.
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Conclusion and perspectives
• The packets method allows better performance on normal light illumination images.
• Color information can be used to improve results on simple grey level images.
• Results need to be confirmed using larger bimodal database (in order to decrease the variance).
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Adaptive color reduction (ACR)
Self organized neural network Reduction adapted to initial distribution of colors
N. Papamarkos, A.E. Atsalakis, and C.P. Strouthopoulos, Adaptive colour reduction, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 32, N°1, , February 2002.