1 Iris Identification Using Wavelet Packets Emine Krichen, Mohamed Anouar Mellakh, Sonia Garcia Salicetti, Bernadette Dorizzi evry.fr Institut National des Télécommunications 9 Rue Charles Fourier, Evry France

2 Outline Classical approach versus our approach (Packets Method) Experimentations on 2 databases Introduction of color information Conclusion and perspectives

3 Introduction Study of iris recognition on normal light illumination –Use of usual devices –Fusion between iris and other biometric modalities (face, eye shape…)

4 Comparison infra-red / normal light Normal light Near Infra red Lack of texture information Presence of a great number of reflections

5 Iris Segmentation Hough Transform (Iris circle)Circular Edge detector

6 Wavelet method 2D wavelet basis : Gabor Spatial parameters in polar coordinates (ρ,θ). 4 resolution levels 2048 coefficients for coding the iris. J. Daugman, “How iris recognition works”, Proceedings of the International Conference on Image Processing, September 2002

7 Our approach : Packet method Process the whole image at each level of resolution Starting with higher mother wavelet window 1664 coefficients for coding iris

8 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.

9 Roc curves (IrisINT) Poor results for the wavelet method The wavelet Packet method is more robust using visible light images

10 Comparative results on CASIA and IrisINT DatabasesIrisINT CASIA Type of errorsFARFRRFARFRR Classical wavelet method2%12.04%0.35%2.08% Packets method0%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 11 Use of color information ACR method Original color image ( different colors) Color image (256 colors) We perform iris recognition using the same algorithm as the one developed for grey level image

12 Use of color information : ROC curve on IrisINT Use of color information allows a better discrimination between the persons.

13 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).

14 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 RGB + neighborhood information One Neuron per color