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

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

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

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) !

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

FLOM-ALMOG IRIS ALGORITHM** Research I.D. Potential Two fold capability: The EYE + The EAR Research in progress @ 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.

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

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

Background - Iris Recognition History An IriScan model 2100 iris scanner

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.

(unpublished recent research) Iris recognition challenges Visible light – ambient light / Acquisition conditions Pupil dilated beyond 7.5 mm Dot (patterned) matrix contact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)

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: http://en.wikipedia.org/wiki/Iris_recognition

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.

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)

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

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)

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.

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

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

A comparison between same iris acquired from different cameras

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

Ear recognition – 3D model

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

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.

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 !

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