1. Iris Biometric Presentation Including Iris/Ear algorithm
FLOM / ALMOG 2012
Iris Biometric Presentation
Including Iris/Ear algorithm
(pat.pending)

2. IRIS for Instant Present I.D.
PREFACE
Within 10 years everyone’s I.D. will be in the form of 3 biometrics:
Fingerprint
Iris
DNA
Metaphorically, regard these 3 biometrics as past, present and future.(Fingerprint-past,DNA-future &forensics);however,
IRIS for Instant Present I.D.
Non contact
Largest degrees of freedom (bits of information)
Stability throughout life

3. Premise
Biometrics has now become the”sine qua non” solution for the protection of privacy and security of individuals; therefore, the 3 most practical biometrics for that purpose because of ease, mobility and non-contact would be:
Voice
Face
Iris

4. Type I Errors (FRR=False Rejection Rate) Voice Face Iris
In case of failure of each of these
what is the backup?
ANS: Only the IRIS has a backup (the other eye) !

5. BLIND Subjects? What constitutes blindness? Cortical Optic nerve
Retinal
Lens
**Corneal is the only “blindness” wherein the iris biometric may be compromised! ( With the other 4 above the iris biometric is functional)
Eye anatomy for the patient SHERING-PLOUGH
Copyright ©
Nucleus Medical Media, Inc.V
serendip

6. FLOM-ALMOG IRIS ALGORITHM**
Research I.D. Potential
Two fold capability:
The EYE +
The EAR
Research in NYU School of Medicine:
ENROLLMENT (inDelivery Room) of New Born Baby’s Ear & Birth-Mother’s Iris followed by RECOGNITION by same biometrics upon Discharge
** THIS algorithm is UNIQUE because of its 2 biometric capablity.

7. Iris Identification Concept based on Spatial Analysis(pat.pend.)
Leonard Flom, M.D. & Ophir Almog, PhD.
Co-Inventors
September 2012

8. Presentation Outline Iris identification up to date Technology
solution
Proposed
solution
summary

9. Background - Iris Recognition History
An IriScan model 2100 iris scanner

10. Iris identification concept – up to date
Gabor Filters are used as a phase of demodulation process to encode iris patterns. The iris is encoded into 8 rings and then polarized into 8 lines rectangular.
The Hamming Distance is used as a recognition rule –
It counts how many pixels are dissimilar between the coded acquired iris and the
compared one.
HD < 0.3 = matching and identification
Images were taken from: Daugman, 2004.
“How Iris Recognition Works” IEEE transaction on circuits and systems for video technology, vol. 14, No. 1, january, 2004.

11. (unpublished recent research)
Iris recognition challenges
Visible light – ambient light /
Acquisition conditions
Pupil dilated beyond 7.5 mm
Dot (patterned)
matrix contact lenses
Scleral lenses
(artificial eyes)
“The Pupil as a Countermeasure to Vulnerabilities of the Iris Biometric” - Leonard Flom, M.D. Clinical Assistant Professor - Ophthalmology
P.I. Biometric Research
(unpublished recent research)

12. Iris identification challenges
Visible light acquisition Is subjected to:
Acquisition conditions – sensitive to ambient light (surrounding bright light may compromise the acquisition of the iris image)
Dark brown eyes have poorer information in visible light in comparison to
IR.
Infra-Red acquisition Is subjected to:
(1) Less detailed information
(2) Fear of IR may deter some subjects
Images were taken from:

13. Iris identification challenges
Recognition rule relies on dissimilarity scalar index (Hamming Distance)
Subjected to acquisition conditions
In a majority of present iris biometrics Recognition fails when the pupil is dilated beyond 7.5mm
Recognition usually fails when someone uses dot (patterned) matrix contact lenses
Scleral lenses (artificial eyes)
are vulnerability to iris recognition
The dissimilarity of the same iris increases due to acquisition conditions
Image was taken from: Daugman, 2004.

14. Scleral lenses -artificial eyes-(no pupil light reflex)
Iris identification challenges
Scleral lenses -artificial eyes-(no pupil light reflex)
Blinking capture to denote live subject (double left click on iris image)

15. Flom&Almog Iris – identification solution
Acquire (RGB) iris images
Find pupil sizes
Convert into gray scale
Apply pattern encoding
Encode interest points and localize them
Correct geometrical noises
Use Hausdorff distance to identify iris

16. LOD (Level of Detail) concept
The image is decomposed into several levels (resolutions / frequencies) through the wavelet transform procedure.
Strong local relations at different levels are indicative of local phenomena (trends).
The algorithm searches the location of each phenomenon and calculates the similarity between all of them.
Radiometric profile of any line among the image of two irises (at different resolution)

17. Iris encoding
Spatial localized descriptors indicates:
Intensity of local radiometric change
Duration of this change
In this innovative algorithm the new coded image is “spatially localized”; therefore, less sensitive to illumination conditions and invariant to pupil dilations.

18. Recognition process detects common localized phenomena
A comparison between same iris encoding - invariant to illumination
Recognition process detects common localized phenomena

19. Recognition is invariant to image size or pupil dilations
A comparison between different iris encoding – invariant to illumination
Recognition is invariant to image size or pupil dilations

20. A comparison between same iris acquired from different cameras

21. “Irides”(Irises) acquired under different devices
Sony alpha
Samsung galaxy s3

22. Ear recognition – 3D model

23. Type II Errors FAR=False Acceptance Rate Voice Face Iris
Impersonation Possibilities ?
Ans:ONLY face and voice may possibly be impersonated.
IMPOSSIBLE with IRIS !

24. System Integration A comparison table between cameras resolution
identification
Sony SLR
Iphone 4s
Samsung S3
Samsung tab
Ipad 3
Cell phones
Web cam
Good
Medium
Poor
System Integration
This table demonstrates that integrating the system (enrollment and .recognition)can easily be accomplished with proper devices.

25. SUMMARY
For practical consideration we compared 3 biometrics.
We introduced a new concept of biometric recognition rule, based on spatial phenomena at different frequencies (resolutions), and it is less sensitive to acquisition conditions.
Our algorithm has the unique capability of enrolling 2 biometrics (iris and ear) Non-existent in any other biometric !

26. Thank you