1 / 14

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 Télécommunications 9 Rue Charles Fourier , 91011 Evry France. Iris Identification Using Wavelet Packets.

diedrick
Download Presentation

Iris Identification Using Wavelet Packets

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Emine Krichen, Mohamed Anouar Mellakh, Sonia Garcia Salicetti, Bernadette Dorizzi {emine.krichen,anouar-mellakh;sonia.salicetti;bernadette.dorizzi}@int-evry.fr Institut National des Télécommunications 9 Rue Charles Fourier , 91011 Evry France Iris Identification Using Wavelet Packets

  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 ofusual devices • Fusion between iris andotherbiometricmodalities (face, eyeshape…)

  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 Circular Edge detector Hough Transform (Iris circle)

  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, 22-25 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 • With infra red illumination, the two methods have quite the same performance. WP is more robust to the presence of eyelids or eyelashes.

  11. 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 C.P. Strouthopoulos, Adaptive color reduction

  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 2002. One Neuron per color RGB + neighborhood information

More Related