Transcript Identification des personnes par la reconnaissance de la

Iris Identification Using
Wavelet Packets
Emine Krichen, Mohamed Anouar Mellakh, Sonia
Garcia Salicetti, Bernadette Dorizzi
{emine.krichen,anouar-mellakh;sonia.salicetti;bernadette.dorizzi}@intevry.fr
Institut National des Télécommunications
9 Rue Charles Fourier , 91011 Evry France
1
Outline
• Classical approach versus our
approach (Packets Method)
• Experimentations on 2 databases
• Introduction of color information
• Conclusion and perspectives
2
Introduction
• Study of iris recognition on normal
light illumination
–Use of usual devices
–Fusion between iris and other
biometric modalities (face, eye
shape…)
3
Comparison infra-red / normal light
Normal light
Near Infra red
• Lack of texture information
• Presence of a great number of reflections
4
Iris Segmentation
Circular Edge detector
Hough Transform (Iris circle)
5
Wavelet method
• 2D wavelet basis : Gabor
iωθ0 φ  r0 ρ 2 α2 θ0 φ 2 β2
 e
 
e
e
Iρ, φρ dρ dφ
• 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
6
Our approach : Packet method
• Process the whole
image at each level
of resolution
• Starting with higher
mother wavelet
window
• 1664 coefficients for
coding iris
7
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.
8
Roc curves (IrisINT)
•Poor results for the wavelet method
•The wavelet Packet method is more
robust using visible light images
9
Comparative results on CASIA
and IrisINT
Databases
Type of errors
Classical wavelet method
Packets method
IrisINT
FAR
2%
0%
FRR
12.04%
0.57%
CASIA
FAR
0.35%
0.2%
FRR
2.08%
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.
10
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
11
Use of color information :
ROC curve on IrisINT
Use of color information allows a better
discrimination between the persons.
12
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).
13
Adaptive color reduction (ACR)
RGB +
neighborhood
information
One
Neuron
per color
Self organized neural network
Reduction adapted to initial distribution of colors
N. Papamarkos, A.E. Atsalakis, and C.P. Strouthopoulos, Adaptive colour reduction, IEEE
14
Transactions on Systems, Man, and Cybernetics, Vol. 32, N°1, , February 2002.