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