1 Iris Identification Using Wavelet Packets Emine Krichen, Mohamed Anouar Mellakh, Sonia Garcia...

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Iris Identification Using Wavelet Packets

Emine Krichen, Mohamed Anouar Mellakh, Sonia Garcia Salicetti, Bernadette Dorizzi

{emine.krichen,anouar-mellakh;sonia.salicetti;bernadette.dorizzi}@int-evry.fr

Institut National des Télécommunications9 Rue Charles Fourier , 91011 Evry France

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Outline

• Classical approach versus our approach (Packets Method)

• Experimentations on 2 databases

• Introduction of color information

• Conclusion and perspectives

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Introduction

• Study of iris recognition on normal light illumination

–Use of usual devices

–Fusion between iris and other biometric modalities (face, eye shape…)

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Comparison infra-red / normal light

Normal light Near Infra red

• Lack of texture information

• Presence of a great number of reflections

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Iris Segmentation

Hough Transform (Iris circle)Circular Edge detector

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Wavelet method

• 2D wavelet basis : Gabor

• Spatial parameters in polar coordinates (ρ,θ).

• 4 resolution levels• 2048 coefficients for

coding the iris.

dφdρρφρ,Ieee22

022

00 βφθαρrφθiω

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.

RGB + neighborhood information

One Neuronper color