human iris biometry

Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions

Human Iris Biometry

Author:J. C. Largo

Supervisor:Jorge L. Villar

Escola Tecnica Superior d’Enginyeria de Telecomunicacio de BarcelonaUniversitat Politecnica de Catalunya

Jul 2016


Outline

1 Introduction to biometrics

2 Iris anatomy

3 Encoding an iris

4 Matching codes

5 Experimental results

6 Conclusions and future work


Introduction to biometrics

Biometrics

The term “biometrics” is related to human characteristics whichcan be used to measure and describe physiological and behaviouralcharacteristics of an individual. These traits are used to identifyand provide reliable automatic recognition of people.

Example

Fingerprint, face, palm print, iris, retina, voice. . .



The overall steps in a biometric system are as follows:

Biometricsample

ROIsegmentation

Featureextraction

Characteristicvector

The characteristic vector is then compared with a database ofpreviously stored vectors to validate the identity of an individual:

Characteristicvector

Matching Decision

Database



Recognition principle

Objects can be reliably classified only if the variability among differentinstances of a given class is significantly less than the variability betweendifferent classes.

Some of the properties that make iris patterns become interestingas an alternative approach to reliable recognition of persons are:they remain stable over time, present enormous variability and arewell protected from the environment.


Iris anatomy

Some important aspects of the iris which are to be considered when

building a biometric system:

The iris has a fixed diameter with an average of 12 mm among thepopulation.

Its formation begins at the 3rd month of gestation and thestructures creating its pattern are complete by the 8th month.

There are two groups of muscles aimed to dilation/contraction: acircular group called the sphincter pupillae, and a radial group calledthe dilator pupillae.


Iris anatomy

Each individual has a uniqueset of iris features which arenot genetically bound.

In NIR wavelengths, evendarkly pigmented irises revealrich and complex features.

It contains features such asarching ligaments, furrows,ridges, crypts, rings, corona,freckles, and a zigzagcollarette.


Encoding an iris

ROIsegmentation

Unwrapthe iris

Featureextraction

Iriscode


ROI segmentation

Locatethe pupil

Locatethe limbus

Identify noniris artifacts

The pupil is located by its attributes: it is dark and circular shaped.

The procedure to locate and parameterize the pupil is:

Biometricsample

Binarization Edgedetection

Parameterizeusing CHT


ROI segmentation

Locatethe pupil

Locatethe limbus


Starting from the pupil, the system searches for the circular path wherethere is maximum change in pixel values of the circular contour:

Pupilparameters

Edgedetection

Searchcircular contour


ROI segmentation

Locatethe pupil

Locatethe limbus


The iris is almost always partially occluded by eyelids, eyelashes andshadows (EES). There are two ways to address EES localization:

Establishing an eyelid curvature model statistically and a commonarc structure to identify eyelashes.

Excluding a predefined region of the iris.

A mask is assigned to each iris code to exclude selected regions of theiris. This regions will later be used to evaluate the texture of the iris thathas not been occluded.


ROI segmentation


Encoding an iris

ROIsegmentation

Unwrapthe iris

Featureextraction

Iriscode


Unwrapping the iris

Robust representations for pattern recognition must be invariant to:

Changes in the size of an eye in the image.

Position of the eye.

Orientation of the patterns.

Variations in pupil size.

For this reasons the following representation is considered:

I(x(r, θ), y(r, θ))→ I(r, θ)

Where r ∈ [0, 1] and θ ∈ [0, 2π] are normalized dimensionless intervals.


Unwrapping the iris

Rubber sheet model

R(r) = (1− r)× rpupil + r × rlimbusx(r, θ) = (1− r)× xpupil + r × xlimbus +R(r)× cos(θ)

y(r, θ) = (1− r)× ypupil + r × ylimbus +R(r)× sin(θ)

Interpolationmeshwork

Applynormalization

Normalizediris


Encoding an iris

ROIsegmentation

Unwrapthe iris

Featureextraction

Iriscode


Feature extraction

Gabor filter

G(x, y|α, β, λ, θ, φ) = e −x′2/α2 − y′2/β2

ej(2πx′/λ+ φ)

x′ = x cos θ + y sin θ y′ = −x sin θ + y cos θ


Encoding an iris

ROIsegmentation

Unwrapthe iris

Featureextraction

Iriscode


Iris code

Each bit in an iris code is computed by evaluating the sign of theprojected local region of the iris image onto a given Gabor filter:

Iris encoding

code(x0, y0|α, β, λ, θ, φ) =

{1 if φ {IG(x0, y0|α, β, λ, θ, φ)} ∈ [0, π]

0 if φ {IG(x0, y0|α, β, λ, θ, φ)} ∈ [−π, 0)

Where φ denotes phase and IG the projection of the normalized irisI(r, θ) on the complex Gabor filters produced by the convolution product:

IG(x, y|α, β, λ, θ, φ) = I(x, y)∗G(x, y|α, β, λ, θ, φ)


Iris code


Matching iris codes

Once the iris is encoded it is time to perform a comparison betweenthe codes to verify the identity of an individual:

Iriscode

Matching Decision

Database


Matching iris codes

The iris codes are matched by computing the relative HammingDistance (HD) score between them.

Each HD score is compared to a certain threshold to validate theidentity of an individual.

(Relative) Hamming Distance (HD)

HD =‖(codeA⊕ codeB)�maskA�maskB‖

‖maskA�maskB‖

Where ⊕ denotes the bitwise operator XOR, � is the bitwise operatorAND and ‖·‖ denotes the Hamming weight as the number of nonzeroelements.


Matching iris codes

As in all pattern recognition systems, decisions are taken accordingto a decision threshold established to meet a certain requirement.

The identity of an individual is accepted if the HD is smaller thanthe threshold.

Hamming Distance (HD)

PD

F

Uncorrelated iris

Same iris

Flase negatives (FN)

False positive (FP)

The overlapping region between both classes causes some identities to bemisclassified.


Independent iris codes

Each bit of an iris code has equal odds of being a 1 or a 0.

The expected proportion of agreeing bits between two different iriscodes is HD=0.5.

The expected distribution of observed Hamming distances betweentwo independent iris codes would be a normalized binomialdistribution with p = 0.5.


Achieving orientation invariance

Problem: intra-class iris codes behave as independent codes if theyare not properly oriented.

Solution: compute the iris phase code and then compare it atseveral discrete orientations by cyclic scrolling the angularcomponent of the code.


Achieving orientation invariance

Consequences: statistically, seeking the best match assuming thatrotated codes behave as independence codes provides a newinter-class distribution with reduced mean.


Experimental results

The database studied was CASIA Iris V1 collected by the Chinese Academy ofSciences. The database presents:

Ns=756 iris images from Neyes=108 eyes with Nrep=7 samples per eye.

The resolution is 320 pixels width and 280 in height.

The operation wavelength is 850 nm in the NIR spectrum.

The total number of comparisons when matching codes is:

Ncomparisons =

(Ns

2

)= 285390.

Ninter−class = Neyes ×

(Nrep

2

)= 2268.

Nintra−class = Ncomparisons −Ninter−class = 283122.


Experimental results

Now, we will compare the database with the anatomic description of thehuman eye. The topics that we will address are:

The non-concentricity of pupil and the limbus.

The distribution of pupil and limbus radii.

The iris orientation distribution along database.


Pupil deviation from the limbus center

Anatomic studies reveal that the pupil centers has a trend of being lowerand nasal with respect to the limbus center.

The pupil center is biased to theleft. This suggests that thesamples were left eyes.

No predisposition on being abovenor below. This may be caused bythe influence of eyelids.


Pupil deviation from the limbus center

It is not uncommon to observe an offset between the pupil and limbuscenter above 20% of the pupil radius.


Distribution of pupil and limbus radii

Anatomically, the pupil radius is variable but the limbus is fixed with anaverage that stays consistent across the human population.

Variations of pupil size do notprovide much information sinceit is intrinsically variable.

Variations in limbus radius can beunderstood as changes in the imageacquisition process (zoom, distanceto the sensor).


Dilation distribution (ρ)

The dilation ratio is defined as:

ρ =pupil diameter

iris diameter

Anatomically, the dilation ratio is bounded ρ ∈ [0.15, 0.75].

The database is mainly composed of pupils with a normal dilation ratio.This is due to the image acquisition process.


Orientation distribution

To avoid excessive rotations that would harm the recognition a statisticaltest was carried to evaluate the distribution of shifts along the database.


Optimizing code parameters

The critical parameters that we will optimized are:

The radial code resolution.

The angular code resolution.

The wavelength λ.

The orientation θ.

The aspect ratio was set to α/β = 1 and the phase offset φ = 0. Tooptimize this parameters the “decidability” criteria was chosen bymeasuring how well separated two distributions are. The “decidability”(d′) criteria is defined as:

d′ =|µ1 − µ2|√

σ21+σ

22

2


Optimizing radial code resolution

Radial resolution is monotonically increasing and thus provides betterdecidability for high resolution codes regardless of the wavelength:

10 20 30 40 50 60 70 80 90 100

Radial resolution

1.5

2

2.5

3

3.5

4D

ecid

abili

ty (

d′)


Optimizing orientation (θ)

We can see that the best orientation is θ = 0 since it reveals all the radialtexture on the iris and thus provides more discriminant information:

0 15 30 45 60 75 90 105 120 135 150 165 180

Orientation ( θ)

2.8

2.9

3

3.1

3.2

3.3

3.4

3.5

3.6

3.7D

ecid

abili

ty (

d′)


Optimizing angular code resolution and wavelength (λ)

Different angular code resolutions provide different optimum scales ofanalysis (λ) since the iris texture has its own frequential informationwhich is defined in the interval θ ∈ [0, 2π].

50 100 150 200 250 300 350 400 450 500

Angular resolution

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Decid

abili

ty (

d′)

λ = 2

λ = 4

λ = 6

λ = 8

λ = 10

λ = 12

λ = 14

λ = 15

λ = 16

λ = 18


Final decision environment

The final optimized decision environment given by λ = 8, α/β = 1,φ = 0, θ = 0, Nrad = 64 radial samples and Nang = 200 angularsamples:


Conclusions and future work

We have the theoretical basis and a first approach to an iris biometricsystem adapted to the NIR spectrum. Further development of thisproject should address:

Accurate segmentation of the eyelashes and eyelids.

Operation under VW spectrum.

Code compaction, reducing correlations within an iris code (mostlyradial).

Combine facial recognition algorithms to extract iris codes.

human iris biometry

Career