May 10, 2004
USVISIT - ITR Project Meeting 1
CSE190a Fall 06
Iris Recognition
Biometrics
CSE 190-a
Lecture 18
Iris
The colored part of the eye is called the iris. It
controls light levels inside the eye similar to the
aperture on a camera. The round opening in the
center of the iris is called the pupil. The iris is
embedded with tiny muscles that dilate (widen) and
constrict (narrow) the pupil size. The sphincter muscle
lies around the very edge of the pupil. In bright light,
the sphincter contracts, causing the pupil to
constrict. The dilator muscle runs radially through the
iris, like spokes on a wheel. This muscle dilates the
eye in dim lighting. The iris is flat and divides the front
of the eye (anterior chamber) from the back of the eye
(posterior chamber). Its color comes from microscopic
pigment cells called melanin.
The color, texture, and patterns of each person's iris
are as unique as a fingerprint.
http://www.stlukeseye.com/anatomy/Iris.asp
Anatomy of the eye
Structure of Eye and location of Iris
http://www.maculacenter.com/eyeanatomy.htm
IrisIris
• Iris is the annular region of the eye responsible for controlling
and directing light to the retina. It is bounded by the pupil and
the sclera (white of the eye); iris is small (11 mm)
• Visual texture of the iris stabilizes during the first two years of
life and carries distinctive information useful for identification
• Each iris is unique; even irises of identical twins are different
© IEEE Computer 2000
Advantages of Iris for RecognitionAdvantages of Iris for Recognition
• Believed to be stable over a person’s
lifetime
• Pattern is epigenetic (not genetically
determined)
• Internal organ, highly protected and
rarely damaged or changed
• Iris patterns possess a high degree of
randomness
• Imaging procedure is non-invasive
• Template size is small
• Image encoding and matching process is
fast
Stability of Iris Pattern
The iris begins to form in the
third month of gestation,
and the trabacular network
creating its pattern are
largely complete by the
eighth month.
Pigment accretion can continue
into the first postnatal years.
Iris color is determined
mainly by the density of
melanin pigment. Blue irises
result from an absence of
pigment.
“The available clinical
evidence indicates that the
trabecular pattern itself is
stable throughout the
lifespan.”
May 10, 2004
USVISIT - ITR Project Meeting 2
Iridology
“Throughout the ages, the eyes have been known as the windows to the soul, and
modern behavioral research is proving this adage to be true. If you look closely
at the iris of the eye, you will notice small, dark dots, light streaks or rounded
openings in the fibers. These characteristics provide the key to unlocking the
mysteries of the personality” (Rayid International).
Iridology
• There is a popular belief in systematic changes in the iris
pattern, reflecting the state of health of each of the organs in
the body, one's mood or personality, and revealing one's
future.
• Iridology resembles palm-reading and is popular in parts of
Romania and in California (According to Daugman).
“All scientific tests dismiss iridology as a medical fraud”
– Berggren, L. (1985), “Iridology: A critical review”,
Acta Ophthalmologica, 63(1): 1-8
Iris under different lighting
• Visible Light
– Layers visible
– Less texture information
– Melanin absorbs visible light
• Infrared Light
– Melanin reflects most
infrared light
– More texture is visible
– Preferred for iris recognition
systems
Infrared Iris Image
In infrared light, even dark brown eyes show rich iris texture
Iris Capturing Devices
• Different Cameras
available:
– Hand held
– Wall mounted
http://www.panasonic.com/business/visionsystems/biometrics.asp
http://www.oki.com/jp/FSC/iris/en/irisgt_h.html
http://www.lgiris.com/products/index.html
May 10, 2004
USVISIT - ITR Project Meeting 3
• The largest deployment of iris
recognition systems is in the
United Arab Emirates (17 air,
land, and sea ports).
• 3.8 billion comparisons are
conducted each day; average time
per match is only a fraction of a
second.
Deployment of Iris Recognition
Frequent Flyers (belonging to EU) are enrolled in the "Privium"
program at Schiphol Airport (NL), enabling them to enter The
Netherlands without presenting their passports .
• German Chancellor Gerhard Schroeder tests the iris recognition
system used for automated passport control at Frankfurt's
international airport, Europe's largest, in August 2004.
• Up to 100 passengers use the service each day to bypass
lengthy lines at regular security checkpoints.
Condominium residents in Tokyo gain entry to the building
using iris patterns, and the elevator is automatically called
and programmed to bring them to their residential floor.
The United Nations High Commission for Refugees administers cash
grants to refugees returning to Afghanistan from surrounding
countries after the fall of the Taleban, using iris patterns in lieu of
any other forms of identification. More than 350,000 persons have
so far been processed by this program using iris recognition.
Iris Representation SchemesIris Representation Schemes
• Daugman
- Gabor Demodulation (PAMI 1993)
• Lim, Lee, Byeon, Kim
- Wavelet Features (ETRIJ 2001)
• Bae, Noh, Kim
- Independent Component Analysis (AVBPA 2003)
• Ma, Tan, Wang, Zhang
- Key local variations (IEEE TIP 2004)
May 10, 2004
USVISIT - ITR Project Meeting 4
Daugman’s Approach
• J. Daugman, “Statistical Richness of Visual Phase Information: Update on Recognizing Persons by
Iris Patterns”, International Journal of Computer Vision, 2001.
• J. Daugman, “Biometric Personal Identification System Based On Iris Analysis”, US Patent
5291560, 1994
Eye Image
Capture
480x640
Iris Localization
and Unwrapping
Feature
Extraction/
Encoding
Comparison
Template Database
Accept/
Reject
Unwrapping
Iris Localization
- Curvilinear Boundaries
Iris Localization
- Curvilinear Boundaries
• Iris is localized using an
integro-differential operator:
• is a smoothing function such as a Gaussian of scale σ
• I(x,y) is the raw input image, and the operator searches for
the maximum in the blurred partial derivative of the image
with respect to an increasing radius r and center co-ordinates
(x0,y0)
• The operator essentially is a circular edge detector and
returns a “spike” when a candidate circle shares the pupil
(iris) center coordinates and radius.
( ) ( ) ( )∫∂∂
00
00
,,
,, 2
,*max
yxr
yxr dsr
yxI
r
rG πσ
( )G rσ
Detected Curvilinear Boundaries Iris Localization
- Eyelid Boundaries
• An approach similar to detecting curvilinear edges is used
to localize both the upper and lower eyelid boundaries
• The path of contour integration in equation (1) is changed
from circular to arcuate, with spline parameters fitted by
standard statistical estimation methods to describe
optimally the available evidence for each eyelid boundary
Detected Eyelid Boundaries Intra-class Variations
Pupil Dilation
(lighting changes)
Inconsistent Iris Size
(distance from the camera)
Eye Rotation
(head tilt)
May 10, 2004
USVISIT - ITR Project Meeting 5
Establishing Coordinate System
Daugman’s Rubber Sheet Model
The model remaps each point within the iris region to a pair of polar coordinates (r,θ)
where r is in the interval [0,1] and θ is angle in [0,2π]
• The model compensates pupil dilation and size inconsistencies
by producing a size- and translation-invariant representation in the
polar coordinate system
• The model does not compensate for rotational inconsistencies,
which is accounted for during matching by shifting the iris templates
in the θ direction until two iris templates are aligned
Centers of iris and pupil coincide Centers of iris and pupil do not coincide
Iris Feature Encoding
• specify position in the image, specify the effective
width and length and is the frequency of the filter
• is the raw iris image in polar coordinate system, and
is a complex valued bit corresponding to the sign of the real and
imaginary parts of filter responses
2 2 2 2
0 0 0( ) ( ) / ( ) /( , ) i r r iG r e e eω θ θ α θ θ βθ − − − − −=
Gabor filtering in polar coordinate system
( , )r θ ( , )α β
even symmetric odd symmetric
2 2 2 2
0 0 0( ) ( ) / ( ) /
{Re,Im} {Re,Im}sgn ( , )
i rg I e e e d dω θ φ ρ α θ φ β
ρφ
ρ φ ρ ρ φ− − − − −= ∫∫
Demodulation and phase quantization
( , )I ρ φ {Re,Im}g
ω
A 1D illustration of the encoding process
* John Daugman’s personal website: http://www.cl.cam.ac.uk/users/jgd1000/
A total of 2,048 bits,
i.e. 256 bytes of
information is
extracted from the
whole iris image
Example of Iris CodingExample of Iris Coding
J.Daugman, “Statistical Richness of Visual Phase Information: Update on Recognizing Persons by
Iris Patterns”, International Journal of Computer Vision, 2001.
Image size is 64 x 256 bytes and the iris code is 8 x 32
bytes; Gabor filter size is 8 x 8
Independence of bits across IrisCodes
• The comparison is done by computing the Hamming
distance between two 256-byte iris codes
• The Hamming Distance between an iris code A and
another code B is given by:
where N=2,048 (256 x 8) if there is no occlusion of the
iris. Otherwise, only valid iris regions are used for
computing the Hamming distance
∑
=
⊗=
N
j
jj BAN
HD
1
1
* Daugman, J. ,"High confidence visual recognition of persons by a test of statistical independence." IEEE Trans. on PAMI, 1993
Iris Code Matching
XOR
May 10, 2004
USVISIT - ITR Project Meeting 6
Hamming distance
• Hamming distance: given two patterns X and Y, the sum of
disagreeing bits (sum of the exclusive-OR between) divided by N,
the total number of bits in the pattern
• If two patterns are derived from the same iris, the Hamming
distance between them will be close to 0.0 due to high correlation
• In order to account for rotational inconsistencies, one template
is shifted left and right bit-wise and a number of Hamming
distance values are calculated from successive shifts.
• The smallest Hamming distance is selected as it corresponds to
the best match between two templates.
1
1 N
j j
j
HD X Y
N =
= ⊗∑
An illustration of iris matching by code shifting
Distribution of Hamming Distances among
Unrelated IrisCodes
The genuine and impostor
Hamming distance distributions
for about 2.3M comparisons
There is hardly any overlap and
hence one can choose a
threshold such that there is
very small probability of error
This experiment shows that iris
indeed is a very good biometric
that can achieve very high
performance Matching Distance Distributions
J. Daugman (1993) "High confidence visual recognition of persons by a test of statistical independence." IEEE Trans. PAMI, vol. 15(11), pp. 1148-1161.
Matching Score DistributionMatching Score Distribution
http://www.cl.cam.ac.uk/users/jgd1000/
Limitations of IrisLimitations of Iris
• Capturing an iris image involves cooperation from the user;
user must stand at a predetermined distance and position in
front of the camera
• Cost of high performance iris systems is relatively high
http://www.oki.com/en/press/2002/z02011e.html
http://news.bbc.co.uk/1/hi/uk/1816221.stm
May 10, 2004
USVISIT - ITR Project Meeting 7
Occlusion (eyelids/eyelashes) Defocus Motion blurred Large pupil
• Iris images may be of poor quality resulting in failures to
enroll
• In a recent test by MPs, up to 7% iris scans could still fail,
due to anomalies such as watery eyes, long eyelashes or
hard contact lenses.
Limitations of IrisLimitations of Iris
• Iris can change over time (e.g., as a result of eye disease),
leading to false rejects.
¾ more than 200,000 cataract operations are performed each
year in UK
¾ about 60,000 people in UK have Nystagmus (tremor of the
eyes)
¾ about 1,000 people in UK have Anaridia (no iris)
• Blind people may fail the test
Limitations of IrisLimitations of Iris
cataract surgery hyphaema (blood clot) iridodialysis
Anti-Spoofing Liveness DetectionAnti-Spoofing Liveness Detection
Contact lens or photograph of a person's iris pattern can be
used to spoof some iris recognition systems
The dot matrix printing process generates four points of spurious
energy in the Fourier plane, corresponding to the directions and
periodicities of coherence in the printing dot matrix, whereas a
natural iris does not have these spurious coherences.
Live VS. Printed Iris
Synthetic Iris Images using
Markov Random Fields
• Texture synthesis approach used
• Input primitives include multiple samples
of partial real iris images
• Synthesis – Primitives blended randomly
to modify an independent random field
(IRF) iteratively
• Result – Iris like image!
Iris Synthesis
• Control
• Iris Image
• Input
• Random Noise Image
• Output
Control Image
Random Image
Synthesized Image
Makthal and Ross, “Synthesizing Iris Images using Markov Random Fields”, EUSIPCO 2005 (Submitted)
May 10, 2004
USVISIT - ITR Project Meeting 8
Multi-layer Synthetic Iris - Lefohn
• Generate a 3D model for iris
• Overlay many semi-transparent layers over the
base layer
• Computer graphics methods used to render these
layers onto the 3D surface
– Different lighting and shadow effects used
– Multiple layer texture generated
Lefohn et. al. “An ocularist's approach to human iris synthesis”, IEEE Computer Graphics and Applications, Dec 2003
Synthesized Images
(a) Base Layer (b) 10 layers superimposed
(c) Synthetic Iris images
Lefohn et. al. “An ocularist's approach to human iris synthesis”, IEEE Computer Graphics and Applications, Dec 2003
Charlie’s Angels (2000) Dracula 2000 (2000)
Is iris recognition worth the trust in the future?
• Photonic and spectrographic countermeasures
¾ spectrographic properties of tissue, fat, and blood
¾ spectrographic properties of melanin pigment
¾ coaxial retinal back-reflection (“red eye” effect)
¾ 4 Purkinje reflections from corneal and lens surfaces
• Behavioral countermeasures
¾ involuntary: autonomic nervous system
hippus (pupillary unrest)
pupillary light reflex (brainstem control)
¾ voluntary: conscious control, challenge responses
eye movements on command
eyelid blinks on command
Liveness DetectionLiveness Detection
United Arab Emirates (UAE) Border Control
• Passengers arriving at all 17 air, land, and sea ports of entry into
UAE today must look into an iris camera
• About 7,000 persons each day take this test; 2,557,000 so far
• Each person is compared against a central ‘Watch List’ of 505, 000
expelled foreigners’ IrisCodes
• Each such exhaustive search of IrisCodes takes about 1 s
• 7,000 x 505,000 IrisCodes = 3.5 billion iris comparisons per day
• Approximately 300 billion iris comparisons performed in this
program to date
• 17,927 matches to the ‘Watch List’ of expellees have been found
• UAE Ministry of Interior says no matches have been disputed; all
confirmed ultimately with other records. False Match Rate = 0.
http://magma.nationalgeographic.com/ngm/afghangirl/
Sharbat Gula, first photographed in 1984 aged 12 in a refugee
camp in Pakistan by National Geographic photographer Steve
McCurry, was traced 18 years later to a remote part of
Afghanistan where she was again photographed by McCurry.
Appeared in national Geographic
National Geographic, 1984 and 2002
May 10, 2004
USVISIT - ITR Project Meeting 9
• Sharbat Gula, first photographed in
1984 aged 12 in a refugee camp in
Pakistan by National Geographic (NG)
photographer Steve McCurry, and
traced 18 years later to a remote part of
Afghanistan where she was again
photographed by McCurry.
• NG turned to the inventor of automatic
iris recognition, John Daugman at the
University of Cambridge.
• The numbers Daugman got left no
question in his mind that the haunted
eyes of the young Afghan refugee and
the eyes of the adult Sharbat Gula
belong to the same person
John Daugman, a professor of computer science at the
University of Cambridge, used his biometric technique to show
that the haunted eyes of the young Afghan refugee and the
eyes of the adult Sharbat Gula belong to the same person.