week10 iris

7/27/2019 Week10 Iris

1/46

Iris Modeling and Synthesis

CPSC 601 Biometric Technologies Course

7/27/2019 Week10 Iris

2/46

Lecture Plan

Motivation

Iris structure

Iris Image acquisition

Methodology Iris Localization

Iris features Matching

Iris Synthesis

Future Developments

7/27/2019 Week10 Iris

3/46

Anatomy of the human iris. The upper panel illustrates the structure ofthe iris seen in a transverse section. The lower panel illustrates thestructure of the iris seen in a frontal sector.

Iris Structure

7/27/2019 Week10 Iris

4/46

At a finer grain of analysis, the iris is composed of several

layers. The posterior surface is composed of heavily

pigmented epithelial cells that make it impenetrable to light.

Anterior to this layer two muscles are located that work in

cooperation to control the size of the pupil.

The visual appearance of the iris is a direct result of itsmultilayered structure. Iris color results from the differential

absorption of light impinging on the pigmented cells in the

anterior border layer.

Iris Structure

7/27/2019 Week10 Iris

5/46

The first source of evidence comes

from clinical observations. Duringthe course of examining largenumber of eyes, ophthalmologistshave noted that the detailedspatial pattern of an Iris seems tobe unique. The pattern seem tovary little, at least past childhood.

The second source of evidencecomes from developmentalbiology. While the generalstructure of the iris is geneticallydetermined, the particulars of itsminutiae are critically dependenton circumstances (e.g. the initialconditions in the embryonic

precursor to the iris).

Anatomy of the iris visible in an optical

image.

Iris Structure - Uniqueness

7/27/2019 Week10 Iris

6/46

Another interesting aspect of the physical characteristics of the iris

from a biometric point of view has to do with its dynamics. Due to the

complex interplay of the iris's muscles, the diameter of the pupil is in a

constant state of small oscillation at a rate of approximately 0.5 Hz.

This movement could be monitored to ensure that a live specimen is

being evaluated.

Since the iris reacts very quickly to changes in impinging illumination,

monitoring the reaction to a controlled illuminant could provide similar

evidence.

Iris Structure - dynamics

7/27/2019 Week10 Iris

7/46

Acquisition of a high-quality iris image,

while remaining non-invasive to human

subjects, is one of the major challengesof automated iris recognition.

Figure: Passive sensing approaches toiris image acquisition. The upperdiagram shows a schematic diagram

of the Daugman image acquisition rig.The lower diagram shows a schematicdiagram of the Wildes et al. imageacquisition.

Iris Image Acquisition

7/27/2019 Week10 Iris

8/46

In order to cope with the inherent variability of ambientillumination, extant approaches to iris image sensing providea controlled source of illumination as a part of their method.

Research initiated at Sarnoff Corporation and subsequentlytransferred to Sensar Incorporated for refinement andCommercialization has yielded the most non-invasive approach toiris image capture that has been documented to date.

For capture, a subject merely needs to stand still and face forwardwith their head in an acquisition volume of 600vertical by

450 horizontal and a distance of approximately 0.38 to 0.76 m, allmeasured from the front-center of the acquisition rig. Capture of an imagethat has proven suitable to drive iris recognition algorithm can then beachieved totally automatically, typically within 2-10 seconds.


7/27/2019 Week10 Iris

9/46

Figure: Active sensing approach to iris image acquisition.


7/27/2019 Week10 Iris

10/46

Following image acquisition, the portion ofthe image that corresponds to the iris

needs to be localized from itssurroundings.

The iris image data can then be brought

under a representation to yield an irissignature for matching against similarlyacquired, localized and represented irises.

Iris Image Localization

7/27/2019 Week10 Iris

11/46

Daugman and Wildes et al. approaches make use of firstderivatives of image intensity to signal the location of edges

that correspond to the borders of the iris. Here, the notionis that the magnitude of the derivative across an imagedborder will show a local maximum due to the local changeof image intensity.

Both systems model the various boundaries that delimit theiris with simple geometric models. For example, they bothmodel the limbus and pupil with circular contours.

The Wildes et al. system also explicitly models the upper andlower eyelids with parabolic arcs. In initial implementation,the Daugman system simply excluded the upper and lowermost portions of the image where eyelid occlusion wasmost likely to occur; subsequent refinements includeexplicit eyelid localization.


7/27/2019 Week10 Iris

12/46

The two approaches differ mostly in the way that they search their

parameter spaces to fit the contour models to the image information. The

Daugman approach fits the circular contours via gradient ascent on the

parameters (xc, y

c, r) so as to maximize

where

is a radial Gaussian with center r0and standard deviation thatsmoothes the image to select the spatial scale of edges under

consideration, *symbolizes the convolution, dsis an element of

circular arc and division by 2rserves to normalize the integral.


7/27/2019 Week10 Iris

13/46

The Wildes et al. approach performs its contour fitting in two steps.First, the image intensity information is converted into a binary edge-map. Second, the edge points vote to instantiate particular contourparameter values.


7/27/2019 Week10 Iris

14/46

Both approaches to localizing the

iris have proven to be successful in

the targeted application. The

histogram-based approach tomodel fitting should avoid problems

with local minima that the active

contour model's gradient descent

procedure might experience.

However, by operating moredirectly with the image derivatives,

the active contour approach

avoids the inevitable thresholding

involved in generating a binary

Edge map.

Illustrative results of iris localization.Given an acquired image, it isnecessary to separate the iris from thesurroundings. Taking as input an irisimage, automated processing

delineates that portion whichcorresponds to the iris.


7/27/2019 Week10 Iris

15/46

The distinctive spatial characteristics of the human iris are displayed at a variety

of scales.

The Daugman approach makes use of a decomposition derived from

application of a two-dimensional version of Gabor filters to the image data.

Since the Daugman system converts to polar coordinates, (r, ), during

matching, it is convenient to give the filters in a corresponding form as

where and co-vary in inverse proportion to generate a set ofquadrature pair frequency selective filters, with center locations specified by (r0,

0). These filters are particularly notable for their ability to achieve good joint

localization in the spatial and frequency domains.

Iris modeling- methodology

7/27/2019 Week10 Iris

16/46

The Wildes et al. approach makes use of an isotropic bandpassdecomposition derived from application of Laplacian of Gaussian (LoG)filters to the image data. The LoG filters can be specified via the form

with the standard deviation of the Gaussian and the radialdistance of a point from the filters center. In practice, the filteredimage is realized as a Laplacian pyramid.


7/27/2019 Week10 Iris

17/46

By retaining only the sign of the Gabor filter output, the

Representational approach that is used by Daugman yields a

remarkably parsimonious representation of an iris. Indeed, arepresentation with a size of 256 bytes can be

accommodated on the magnetic stripe affixed to the back

of standard credit/debit cards. In contrast, the Wildes et

al. representation is derived directly from the filteredimage for size on the order of the number of bytes in the

iris region of the originally captured image.


7/27/2019 Week10 Iris

18/46

Iris matching can be understood as a three-stage process.

The first stage is concerned with establishing a spatialcorrespondence between two iris signatures that are to be

compared.

Given correspondence, the second stage is concernedwith quantifying the goodness of match between two irissignatures.

The third stage is concerned with making a decisionabout whether or not two signatures derive from the same physicaliris, based on the goodness of match.

Iris matching

7/27/2019 Week10 Iris

19/46

Given the combination of required subject participation and thecapabilities of sensor platforms currently in use, the keygeometric degrees of freedom that must be compensated for inthe underlying iris data are shift, scaling and rotation.Shift accounts for offsets of the eye in the plane parallel to thecamera's sensor array. Scale accounts for offsets along thecamera's optical axis. Rotation accounts for deviation in angularposition about the optical axis. Another degree of freedom ofpotential interest is that of pupil dilation.

Iris matching

7/27/2019 Week10 Iris

20/46

Daugmans system uses radial scaling to compensate for overall size

as well as a simple model of pupil variation based on linear stretching.

The scaling serves to map Cartesian image coordinates (x, y) to polar

image coordinates (r, ) according to

where r lies on [0, 1] and is cyclic over [0, 2], while (xp(), yp())

and (x1(), y1()) are the coordinates of the pupillary and limbicboundaries in the direction . Rotation is compensated for by brute

force search: explicitly shifting an iris signature in by various

amounts during matching.

Iris matching

7/27/2019 Week10 Iris

21/46

The Wildes et al. approach uses an image registration technique to

compensate for both scaling and rotation. This approach geometrically projects

an image, Ia(x, y), into alignment with a comparison image, Ic(x, y), according

to a mapping function (u(x, y), v(x, y)) such that, for all (x, y), the image

intensity value at (x, y)(u(x, y), v(x, y)) in Iais close to that at (x, y) in Ic.

More precisely, the mapping function (u, v) is taken to minimize

while being constrained to capture a similarity transformation of image

coordinates (x, y) to (x, y), i.e.

with sa scaling factor and R()a matrix representing rotation by .

Iris matching

7/27/2019 Week10 Iris

22/46

An appropriate match metric can be based on direct point wise

Comparisons between primitives in the corresponding signature

representations. The Daugman approach quantifies this matter by

computing the percentage of mismatched bits between a pair of irisrepresentations, i.e. the normalized Hamming distance. Letting A and B be

two iris signatures to be compared, this quantity can be calculated as

With subscriptj indexing bit position anddenoting the exclusive-OR operator. The Wildes et al. system employs a

somewhat more elaborate procedure to quantify the goodness of match.

The approach is based on normalized correlation between two signatures

(i.e. pyramid representations) of interest.

Iris matching

7/27/2019 Week10 Iris

23/46

The final subtask of matching is to evaluate the goodness ofmatch values to make a final judgement as to whether twosignatures under consideration do (authentic) or do not(impostor) derive from the same physical iris. In theDaugman approach, this amounts to choosing a separationpoint in the space of (normalized) Hamming distancesbetween the iris signatures. Distances smaller than theseparation point will be taken as indicative of authentics;those larger will be taken as indicative of impostors.

In the Wildes et al. approach, the decision making processmust combine the four goodness of match measurementsthat are calculated by the previous stage of processing (i.e.one for each pass band in the Laplacian pyramidrepresentation that comprises a signature) into a single

accept/reject judgement.

Iris matching

7/27/2019 Week10 Iris

24/46

Further developments could befocused on yielding more compactSystems that can be easilyincorporated into consumer productswhere access control is desired (e.g.automobiles, personal computers,various handheld devices).

Can iris recognition be performed atgreater subject to sensor distanceswhile remaining unobtrusive?

How much subject motion can betolerated during image capture?

Can performance be made morerobust to uncontrolled ambientillumination?

Toward iris recognition at a distance.An interesting direction for futureresearch in iris recognition is to relaxconstraints observed by extantsystems. As a step in this direction, aniris image captured at 10m subject to

sensor distance is shown.

Iris modelingfuture developments

7/27/2019 Week10 Iris

25/46

At a more operational level of performance analysis, studies ofiris recognition systems need to be performed whereindetails of acquisition are systematically manipulated,documented and reported. Parameters of interest include,geometric and photometric aspects of the experimentalstage, length of time monitored and temporal lag betweentemplate construction and recognition attempt. Similarly,details of captured irises and relevant personal accessoriesneed to be properly documented in these same studies

(e.g. eye color, eyewear).

Iris modelingfuture developments

7/27/2019 Week10 Iris

26/46

Iris Synthesis

Classical Biometrics - Recognition Fingerprints, Faces, Irises

Inverse Problem Synthesis

Testing Recognition methods

7/27/2019 Week10 Iris

27/46

Iris Synthesis - Goals

Synthesis Of Biometric Databases

Iris Database Augmentation

Testing Recognition Methods

Minimal User Input

7/27/2019 Week10 Iris

28/46

Iris Synthesis - previous work

Iris Recognition - [Wildes 94, Daugman 04]

Biometric Synthesis - [Yanushkevich et al.04]

Iris Synthesis - [Lefohn et al. 03, Cui et al. 04]

7/27/2019 Week10 Iris

29/46

Iris Synthesis

An Ocularists Approach to Human

Iris Synthesis. [ Lefohn et. al. 03]

An Iris image synthesis method

based on PCA and Super-Resolution. [Cui et. al. 04]

7/27/2019 Week10 Iris

30/46

PCA Approach

Uses 75 Dimensional PCA Feature

Vector Randomization

Super Resolution

Great statistical results Low Realism

7/27/2019 Week10 Iris

31/46

Ocularists Approach

Uses: 30-70

Layers Great Results.

Domain Specific

KnowledgeAn ocularist's approach to human iris synthesis. Lefohn et. al. 2003.

Used with permission.

7/27/2019 Week10 Iris

32/46

New Approach

Use Real Iris Sample Use sets of Similar Irises

Capture Characteristics Chaikin Reverse Subdivision

Combine Characteristics Multiple Iris Donors

See L. Wecker, F. Samavati and M. Gavrilova workson the subject.

7/27/2019 Week10 Iris

33/46

Comparison

PCA Global Features

Not as Efficient

Realism

Reverse

Subdivision Global & Local Features

Linear Implementation

Realistic results

7/27/2019 Week10 Iris

34/46

Organization

7/27/2019 Week10 Iris

35/46

Method

First step: Isolate the iris.

Polar Transform

Iris Stretching

7/27/2019 Week10 Iris

36/46

Multiresolution

Data has many resolutions Levels of resolution have different meanings

Reverse Subdivision Details

7/27/2019 Week10 Iris

37/46

Decomposition

7/27/2019 Week10 Iris

38/46

Method

Capture Details Reverse Subdivision

Details All Characteristics

Courtesy of: Michal Dobes and Libor Machala,

Iris Database, http://www.inf.upol.cz/iris/
http://www.inf.upol.cz/iris/http://www.inf.upol.cz/iris/http://www.inf.upol.cz/iris/

7/27/2019 Week10 Iris

39/46

Combinations

7/27/2019 Week10 Iris

40/46

Classifications

Frequency of Data

Number of Concentric Rings

Courtesy of: Michal Dobes and Libor Machala, Iris Database, http://www.inf.upol.cz/iris/

7/27/2019 Week10 Iris

41/46

Database Size

7/27/2019 Week10 Iris

42/46


Original SetInput Irises

7/27/2019 Week10 Iris

43/46


Output Irises

7/27/2019 Week10 Iris

44/46

Combinations

7/27/2019 Week10 Iris

45/46

Output Irises


7/27/2019 Week10 Iris

46/46

Future Work

Post-Processing

Multiple samples of each iris

Verification

Statistically

week10 iris

Documents