A low-cost video-based iris recognition system YESS’09, July 8-9 2009, Washington

Stéphane Derrode, William Ketchantang, salah Bourennane and Lionel MartinInstitut Fresnel (UMR 6133), Ecole Centrale Marseille, France

stephane.derrode@centrale-marseille.fr

Pupil detection & localization

Quality irisimage selection

Iris signatureextraction

Database

Enrolment

Irislocalization

Prediction of nextpupil localization

Matching

Accept Reject

t-1

t

t+1

High

PoorIris signature extraction

Pupil and iris localization

Acknowledgment:

The authors would like to thank to PACA region (France), and ST MicroElectronics for financial support. Our tests have been partly conducted on videos coming from the EC Funded CAVIAR project/IST 2001 37540. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/.

US Patent Applications: L. Martin, G. Petitjean, S. Derrode, W. Ketchantang. Method and device for locating a human iris in an eye image. No. 2008/0273,763, STMicroelectronics SA; Univ. Paul Cézanne Aix-Marseille III. June 11, 2008.

• S. Derrode et F. Ghorbel, Robust and efficient Fourier-Mellin transform approximations for invariant grey-level image description and reconstruction, Computer Vision and Image Understanding, Vol. 83(1), pp. 57-78, juillet 2001.

Prototype

LCD

Video camera

LED (770nm)Morphological operations(Specular highlight).

Coarse loc. of dark region

BATH CASIA ST

Iris image and artefacts

Video acquisition

Focused Motion blur Defocus blurBlink

total occlusion Blink

partial occlusion

Pupil

Sclera

Pupillary area

Ciliary area

Collarette

Iris

ST Microelectronics

Eyelashes

Eyelid

Specular highlight

US Patent Applications: L. Martin, W. Ketchantang, S. Derrode. Method and device for selecting images in a sequence of iris images received in a stream. No. 2008/0075,335, STMicroelectronics SA; Univ. Paul Cézanne Aix-Marseille III. March, 27 2008.

« On-the-fly » iris quality check

Frame 0 Frame 243 Frame 303 Frame 518

IQS = IFS + BPD + V(t|t-1)-1

Iris QualityScore

Circular freq. in collarette

Black pixel

density

Pupil relative velocity(Kalman filter)

Adaptative histogramthresholding.

Pupil loc.(Cp, Rp)

Analysis of 1D horiz. intensity profile passing through Cp.

Iris loc.(Ci, Ri)

Acquisition of a workable iris image requires a strict cooperation of the user. Iris images generally show partial occlusions due to eyelids and eyelashes, blur, light reflections due to lens and glasses uses… Usually, it requires several attempts to get a workable image.

The system (see prototype) is made of a CMOS webcam-type camera with an optic and an IR light source.

• Camera: 640 x 480, no autofocus, no automatic gain.• Optic: Focal length: 25 mm; Aperture: 2.5; FOV: H=10°;

V=8°; Distance to focus: 20 cm• Light: Infrared GaAIAs LED - 770 nm

The LCD helps the user to furnish a correct image of its eye.

Iris code

Fourier-Mellin transform Invariance to rotation and dilatation

Area selection

Encoding J. Daugman

N signatures

N images