ppt on iris scan

Automated Iris Recognition Technology &

Iris Biometric System

CS 790Q Biometrics

Instructor: Dr G. Bebis

Presented by Chang Jia

Dec 9th, 2005

2

Overview

The Iris as a Biometrics: The iris is an overt body that

is available for remote assessment with the aid of a

machine vision system to do automated iris

recognition.

Iris recognition technology combines computer vision,

pattern recognition, statistical inference, and optics.

The spatial patterns that are apparent in the human iris

are highly distinctive to an individual. Clinical observations

Developmental biology

3

The structure of the human eyeThe structure of the iris seen in a transverse section

The structure of the iris seen in a frontal section

Overview

4

Its suitability as an exceptionally accurate biometric

derives from its extremely data-rich physical structure

genetic independence — no two eyes are the same

patterns apparently stable throughout life

physical protection by a transparent window (the

cornea), highly protected by internal organ of the eye

externally visible, so noninvasive — patterns imaged

from a distance

Overview

5

The disadvantages to use iris as a biometric

measurement are Small target (1 cm) to acquire from a distance (about 1

m)

Moving target

Located behind a curved, wet, reflecting surface

Obscured by eyelashes, lenses, reflections

Partially occluded by eyelids, often drooping

Deforms non-elastically as pupil changes size

Illumination should not be visible or bright

Overview

PART I:Iris Recognition: An Emerging Biometric Technology

R. Wildes, "Iris Recognition: An Emerging Biometric Technology", Proceedings of the IEEE, vol 85, no. 9, pp. 1348-1363, 1997.

CS 790Q Biometrics

7

Outline

Technical Issues ** Image Acquisition

Iris Localization

Pattern Matching

Systems and Performance

** (Throughout the discussion in this paper, the iris-recognition s

ystems of Daugman and Wildes et al. will be used to provide illu

strations.)

8

Technical Issues

Schematic diagram of iris recognition

9

I. Image Acquisition

Why important?

One of the major challenges of automated iris

recognition is to capture a high-quality image of the iris

while remaining noninvasive to the human operator.

Concerns on the image acquisition rigs Obtained images with sufficient resolution and sharpness

Good contrast in the interior iris pattern with proper

illumination

Well centered without unduly constraining the operator

Artifacts eliminated as much as possible

10

The Daugman image-acquisition rig

I. Image Acquisition - Rigs

11

The Wildes et al. image-acquisition rig

I. Image Acquisition - Rigs

12

Result Image from Wildes et al. rig -- capture the iris as part of a larger image that also contains data derived from the immediately surrounding eye region

I. Image Acquisition - Results

13

In common: Easy for a human operator to master Use video rate capture

Difference: Illumination

The Daugman’s system makes use of an LED-based point light source in conjunction with a standard video camera.

The Wildes et al. system makes use of a diffuse source and polarization in conjunction with a low-light level camera.

Operator self-position The Daugman’s system provides the operator with live video fee

dback The Wildes et al. system provides a reticle to aid the operator in

positioning

Discussion

14

Purpose: to localize that portion of the acquired imag

e that corresponds to an iris

In particular, it is necessary to localize that portion of

the image derived from inside the limbus (the border

between the sclera and the iris) and outside the pupil.

Desired characteristics of iris localization:

Sensitive to a wide range of edge contrast

Robust to irregular borders

Capable of dealing with variable occlusions

II. Iris Localization

15

The Daugman system fits the circular contours via gradie

nt ascent on the parameters so as to maximize

Where is a radial Gaussian,

and circular contours (for the limbic and pupillary bounda

ries) be parameterized by center location (xc,yc), and radi

us r (active contour fitting method)


16

The Wildes et al. system performs its contour fitting in tw

o steps. (histogram-based approach) First, the image intensity information is converted into a binary e

dge-map

where

and

Second, the edge points vote to instantiate particular contour par

ameter values.


17

The voting procedure of the Wildes et al. system is realized via Hough transforms on parametric definitions of the iris boundary contours.


18

Illustrative Results of Iris Localization

Obtained by using the Wildes et al. system

only that portion of the image below the upper eyelid and above the lower

eyelid should be included

19

Both approaches are likely to encounter difficulties if r

equired to deal with images that contain broader regi

ons of the surrounding face than the immediate eye r

egion

Difference: the active contour approach avoids the inevitable thres

holding involved in generating a binary edge-map

the histogram-based approach to model fitting should a

void problems with local minima that the active contour

model’s gradient descent procedure might experience

Discussion

20

Four steps:

1) bringing the newly acquired iris pattern into spatial

alignment with a candidate data base entry;

2) choosing a representation of the aligned iris patterns

that makes their distinctive patterns apparent;

3) evaluating the goodness of match between the newly

acquired and data base representations;

4) deciding if the newly acquired data and the data base

entry were derived from the same iris based on the

goodness of match.

III. Pattern Matching

21

Purpose: to establish a precise correspondence

between characteristic structures across the two

images.

Both of the systems under discussion compensate for

image shift, scaling, and rotation.

For both systems, iris localization is charged with

isolating an iris in a larger acquired image and thereby

accomplishes alignment for image shift.

III. Pattern Matching -Alignment

22

The Daugman’s system uses radial scaling to compensate for overall size as well as a simple model of pupil variation based on linear stretching.


while being constrained to capture a similarity transformation of image coordinates (x, y) to (x’, y’)

Map Cartesian image coordinates (x, y) to dimensionless polar (r, ө)image coordinates according to

The Wildes et al. system uses an image-registration technique to compensate for both scaling and rotation. The mapping function (u,v) is to minimize

23

The two methods for establishing correspondences

between acquired and data base iris images seem to

be adequate for controlled assessment scenarios

Improvements:

more sophisticated methods may prove to be

necessary in more relaxed scenarios

more complicated global geometric

compensations will be necessary if full perspective

distortions (e.g., foreshortening) become

significant


24

The Daugman’s system uses demodulation with complex-valued 2D Gabor wavelets to encode the phase sequence of the iris pattern to an “IrisCode”.

III. Pattern Matching - Representation

25

In implementation, the Gabor filtering is performed via a relaxation algorithm, with quantization of the recovered phase information yielding the final representation.


Pictorial Examples of one IrisCode

26

The Wildes et al. system makes us of an isotropic bandpass decomposition derived from application of Laplacian of Gaussian filters to the image data.

In practice, the filtered image is realized as a Laplacian pyramid. This representation is defined procedurally in terms of a cascade of small Gaussian-like filters.


with σ the standard deviation of the Gaussian and ρ the radial distance of a point from the filter’s center

27

Result: Multiscale representation for iris pattern matching. Distinctive features of the iris are manifest across a range of spatial scales.


Obtained by using the Wildes et al. system

28

The Daugman system computes the normalized Hamming distance as

The result of this computation is then used as the goodness of match, with smaller values indicating better matches.

IV. Pattern Matching – Goodness of Match

29

The Wildes et al. system employs normalized correlation between the acquired and data base representations.

IV. Pattern Matching - Decision

30


For the Daugman system, this amounts to choosing a separation point in the space of (normalized) Hamming distances between iris representations.

In order to calculate the cross-over point, sample populations of imposters and authentics were each fit with parametrically defined distributions.

31

For the Wildes et al. system, the decision-making process must combine the four goodness-of-match measurements that are calculated by the previous stage of processing (i.e., one for each pass band in the Laplacian pyramid representation) into a single accept/reject judgment.


32

Both the enrollment and verification modes take under 1s to

complete.

Empirical test 1: 592 irises from 323 persons the system

exhibited no false accepts and no false rejects

Empirical test 2:

Phase1: 199 irises from 122 persons, 878 attempts in

identification mode over 8 days no false accepts and 89

false rejects (47 retry with still 16 rejected)

Phase2: 96 irises (among 199) with 403 entries for

identification no false accepts and no false rejects

Systems and Performance - The Daugman iris-recognition system

33

Both the enrollment and verification modes require ap

proximately 10s to complete.

Only one empirical test: 60 different irises with 10 ima

ges each (5 at the beginning and 5 about one month l

ater) from 40 persons no false accepts and no fals

e rejects.

Systems and Performance - The Wildes et al. iris-recognition system

34

Questions?

PART II:An Iris Biometric System for Public and Personal Use

M. Negin et al., "An Iris Biometric System for Public and Personal Use", IEEE Computer, pp. 70-75, February 2000.

CS 790Q Biometrics

36

Iris identification process

The system captures a digital image of one eye, encodes its iris pattern, then matches that file against the file stored in the database for that individual.

37

The public-use system

The public-use multiple-camera system for correctly positioning and imaging a subject’s iris.

Note: wide-field-of-view (WFOV) & narrow-field-of-view (NFOV) camera

38

The public-use optical platform

(a) left and right illuminator pods, gaze director, and optical filter

(b) a solid model of the platform’s internal components.

39

The user manually positions the camera three to four inches in front of the eye.

Make sure that the device’s LED centers within the aperture that superimposes the user’s line of sight and the camera’s optical axis.

The personal-use system

40

Identification Performance

Verification distributions of authentic results (in brown) and imposter results (in green).

41

Field Trial Experience

The first pilot program—with the Nationwide Building Societ

y in Swindon, England—ran for six months and included mo

re than 1,000 participants, before going into regular service

during the fourth quarter of 1998.

The field trial experience has been very positive: • 91 percent prefer iris identification to a PIN (personal identification nu

mber) or signature,

• 94 percent would recommend iris identification to friends and family,

• 94 percent were comfortable or very comfortable using the system.

The survey also found nearly 100 percent approval on three

areas of crucial importance to consumers: reliability, securit

y, and acceptability.

42

Thank You.

Questions?

ppt on iris scan

Documents