March 10, 2004 1

Iris Recognition

Instructor: Natalia Schmid

BIOM 426: Biometrics Systems

March 10, 2004 2

Outline

• Anatomy • Iris Recognition System • Image Processing (John Daugman) - iris localization - encoding • Measure of Performance • Results • Other Algorithms • Pros and Cons • Ongoing Work at WVU • References

March 10, 2004 3

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.

March 10, 2004 4

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.

March 10, 2004 5

Individuality of Iris

Left and right eye irises have distinctive pattern.

March 10, 2004 6

Iris Recognition System

LocalizationAcquisition

IrisCode Gabor Filters Polar Representation

Image

Demarcated Zones

March 10, 2004 7

Iris Imaging • Distance up to 1 meter

• Near-infrared camera

• Mirrow

March 10, 2004 8

Imaging Systems

http://www.iridiantech.com/

March 10, 2004 9

Imaging Systems

http://www.iridiantech.com/

March 10, 2004 10

Image Processing

John Daugman (1994)

• Pupil detection: circular edge detector

• Segmenting sclera

0000

,,,, 2

),()(max

yxryxr

dsr

yxI

rrG

8/

8/]10,5.1[

),(2

max00

ddIrr

r

rrrr

March 10, 2004 11

Rubbersheet Model

rr

0 1

θ

θEach pixel (x,y) is mapped into polar pair (r, ).

θ

Circular band is divided into 8 subbands of equal thickness for a given angle .

Subbands are sampled uniformly in and in r.

Sampling = averaging over a patch of pixels.

θ

θ

March 10, 2004 12

Encoding

2

20

2

20

0

)()()(2exp),(

ba

rrirG

2-D Gabor filter in polar coordinates:

1

0

0

9.0

1

0

0

r

b

a

March 10, 2004 13

IrisCode Formation

Intensity is left out of consideration. Only sign (phase) is of importance.

256 bytes2,048 bits

March 10, 2004 14

Measure of Performance

• Off-line and on-line modes of operation.

Hamming distance: standard measure for comparison of binary strings.

k

n

kk yx

nD

1

1

x and y are two IrisCodes

is the notation for exclusive OR (XOR)

Counts bits that disagree.

March 10, 2004 15

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.

Strong radial dependancies Some angular dependencies

March 10, 2004 16

ObservationsRead J. Daugman’s statement with caution. Interpret correctly.

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

March 10, 2004 17

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.

March 10, 2004 18

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.

March 10, 2004 19

Decision

Non-ideal conditions:

The same eye distributions depend strongly on the quality of imaging.

- motion blur - focus - noise - pose variation - illumination

March 10, 2004 20

DecisionIdeal 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

March 10, 2004 21

Error Probabilities

HD Criterion Odds of False Accept Odds of False Reject

0.28 1 in 1210 1 in 11,400 0.29 1 in 1110 1 in 22,700 0.30 1 in 6.2 billion 1 in 46,000 0.31 1 in 665 million 1 in 95,000 0.32 1 in 81 million 1 in 201,000 0.33 1 in 11.1 million 1 in 433,000 0.34 1 in 1.7 million 1 in 950,000

0.342 Cross-over 1 in 1.2 million 1 in 1.2 million 0.35 1 in 295,000 1 in 2.12 million 0.36 1 in 57,000 1 in 4.83 million 0.37 1 in 12,300 1 in 11.3 million

Biometrics: Personal Identification in Networked Society, p. 115

March 10, 2004 22

False Accept Rate

FMRNFMRFAR N )1(1

For large database search: - FMR is used in verification - FAR is used in identification

)(log01.032.0 10 NHDcrit

Adaptive threshold: to keep FAR fixed:

March 10, 2004 23

Test Results

http://www.cl.cam.ac.uk/users/jgd1000/iristests.pdf

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).

March 10, 2004 24

Performance Comparison

UK National Physical Laboratory test report, 2001.

http://www.cl.cam.ac.uk/users/jgd1000/NPLsummary.gif

March 10, 2004 25

Best-of-3 error rates

UK National Physical Laboratory test report, 2001.

Performance Comparison

March 10, 2004 26

http://www.abc.net.au/science/news/stories/s982770.htm

Future of Iris

March 10, 2004 27

References1. 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. 6.