Transcript Iris Recognition Slides adapted from Natalia Schmid and John Daugman Outline • Anatomy • Iris Recognition System • Image Processing (John Daugman) - iris localization -

Iris Recognition
Slides adapted from Natalia Schmid and John Daugman
Outline
• Anatomy
• Iris Recognition System
• Image Processing (John Daugman)
- iris localization
- encoding
• Measure of Performance
• Results
• Pros and Cons
• References
Anatomy of the Human Eye
• Eye = Camera
• Cornea bends, refracts,
and focuses light.
• Retina = Film for image
projection (converts image
into electrical signals).
• Optical nerve transmits
signals to the brain.
Structure of Iris
• Iris = Aperture
• Different types of muscles:
- the sphincter muscle
(constriction)
- radial muscles (dilation)
• Iris is flat
• Color: pigment cells called
melanin
• The color texture, and patterns
are unique.
Individuality of Iris
Left and right eye irises have distinctive pattern.
Iris Recognition System
Acquisition
IrisCode
Gabor Filters
Image
Localization
Polar Representation
Demarcated Zones
Iris Imaging
• Distance up to 1 meter
• Near-infrared camera
Imaging Systems
http://www.iridiantech.com/
Imaging Systems
http://www.iridiantech.com/
Image Processing
John Daugman (1994)
• Pupil detection: circular edge
detector

I ( x, y )
max G (r )
ds

r , x0 , y 0
r r , x0 , y0 2r
• Segmenting sclera
r 
  / 8

2
max
I (  , ) dd


r[1.5 r0 ,10 r0 ] r
  r   r    / 8
Rubbersheet Model
θ
r
0
1
Each pixel (x,y) is mapped into
polar pair (r, θ).
r
θ
Circular band is divided into 8
subbands of equal thickness
for a given θ angle.
Subbands are sampled
uniformly in θ and in r.
Sampling = averaging over a
patch of pixels.
Encoding
2-D Gabor filter in polar coordinates:

(r  r0 ) 2 (   0 ) 2 

G(r , )  exp  2 i (   0 ) 

2
2
a
b


a 1
b  0 .9
r0  0
0  0
 1
IrisCode Formation
Intensity is left out of consideration.
Only sign (phase) is of importance.
256 bytes
2,048 bits
Measure of Performance
• Off-line and on-line modes of operation.
Hamming distance: standard measure for comparison of binary
strings.
1 n
D   xk  y k
n k 1
x and y are two IrisCodes

is the notation for exclusive OR (XOR)
Counts bits that disagree.
Observations
• Two IrisCodes from the same
eye form genuine pair => genuine
Hamming distance.
• Two IrisCodes from two
different eyes form imposter pair
=> imposter Hamming distance.
• Bits in IrisCodes are correlated
(both for genuine pair and for
imposter pair).
• The
correlation
between
IrisCodes from the same eye is
stronger.
Observations
The fact that this distribution
is uniform indicates that
different irises do not
systematically share any
common structure.
For example, if most irises
had a furrow or crypt in the
12-o'clock position, then the
plot shown here would not
be flat.
URL: http://www.cl.cam.ac.uk/users/jgd1000/independence.html
Degrees of Freedom
Imposter matching score:
- normalized histogram
- approximation curve
- Binomial with 249 degrees
of freedom
Interpretation: Given a
large number of imposter
pairs. The average number
of distinctive bits is equal to
249.
Histograms of Matching Scores
Decidability Index d-prime:
d-prime = 11.36
The cross-over point is
0.342
Compute FMR and FRR for
every threshold value.
Decision
The same eye distributions depend strongly on the quality of imaging.
Non-ideal conditions:
- motion blur
- focus
- noise
- pose variation
- illumination
Decision
Ideal conditions:
Imaging quality
determines how much the
same iris distribution
evolves and migrates
leftwards.
d-prime for ideal
imaging:
d-prime = 14.1
d-prime for non-ideal
imaging (previous slide):
d-prime = 7.3
Error Probabilities
HD Criterion
0.28
0.29
0.30
0.31
0.32
0.33
0.34
0.342 Cross-over
0.35
0.36
0.37
Odds of False Accept
1 in 1012
1 in 1011
1 in 6.2 billion
1 in 665 million
1 in 81 million
1 in 11.1 million
1 in 1.7 million
1 in 1.2 million
1 in 295,000
1 in 57,000
1 in 12,300
Odds of False Reject
1 in 11,400
1 in 22,700
1 in 46,000
1 in 95,000
1 in 201,000
1 in 433,000
1 in 950,000
1 in 1.2 million
1 in 2.12 million
1 in 4.83 million
1 in 11.3 million
Biometrics: Personal Identification in Networked Society, p. 115
False Accept Rate
For large database search:
- FMR is used in verification
- FAR is used in identification
FAR  1  (1  FMR) N  N  FMR
Adaptive threshold: to keep FAR fixed:
HDcrit  0.32  0.01 log10 ( N )
Test Results
The results of tests
published in the
period from 1996 to
2003.
Be cautious about
reading these numbers:
The middle column
shows the number of
imposter pairs tested
(not the number of
individuals per dataset).
http://www.cl.cam.ac.uk/users/jgd1000/iristests.pdf
Performance Comparison
UK National Physical Laboratory test report, 2001.
http://www.cl.cam.ac.uk/users/jgd1000/NPLsummary.gif
Cons
 There are few legacy databases. Though iris may be a good
biometric for identification, large-scale deployment is impeded
by lack of installed base.
 Since the iris is small, sampling the iris pattern requires much
user cooperation or complex, expensive input devices.
 The performance of iris authentication may be impaired by
glasses, sunglasses, and contact lenses; subjects may have to
remove them.
 The iris biometric, in general, is not left as evidence on the
scene of crime; no trace left.
Pros
 Iris is currently claimed and perhaps widely believed to be the
most accurate biometric, especially when it comes to FA rates.
Iris has very few False Accepts (the important security aspect).
 It maintains stability of characteristic over a lifetime.
 Iris has received little negative press and may therefore be more
readily accepted. The fact that there is no criminal association
helps.
 The dominant commercial vendors claim that iris does not
involve high training costs.
Future of Iris
http://www.abc.net.au/science/news/stories/s982770.htm
National Geographic: 1984 and 2002
Sharbat Gula

The remarkable story of 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.

So the 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
eyes of the young Afghan
refugee and the eyes of the adult
Sharbat Gula belong to the
same person.
John Daugman and the Eyes of Sharbat Gula
References
1.
J. Daugman’s web site. URL: http://www.cl.cam.ac.uk/users/jgd1000/
2.
J. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical
Independence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no.
11, pp. 1148 – 1161, 1993.
3.
J. Daugman, United States Patent No. 5,291,560 (issued on March 1994). Biometric
Personal Identification System Based on Iris Analysis, Washington DC: U.S.
Government Printing Office, 1994.
4.
J. Daugman, “The Importance of Being Random: Statistical Principles of Iris
Recognition,” Pattern Recognition, vol. 36, no. 2, pp 279-291.
5.
R. P. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proc. of the IEEE,
vol. 85, no. 9, 1997, pp. 1348-1363.