Download - 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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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 Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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. . .
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Introduction to biometrics
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Introduction to biometrics
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Encoding an iris
ROIsegmentation
Unwrapthe iris
Featureextraction
Iriscode
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Encoding an iris
ROIsegmentation
Unwrapthe iris
Featureextraction
Iriscode
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
ROI segmentation
Locatethe pupil
Locatethe limbus
Identify noniris artifacts
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
ROI segmentation
Locatethe pupil
Locatethe limbus
Identify noniris artifacts
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
ROI segmentation
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Encoding an iris
ROIsegmentation
Unwrapthe iris
Featureextraction
Iriscode
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Encoding an iris
ROIsegmentation
Unwrapthe iris
Featureextraction
Iriscode
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Encoding an iris
ROIsegmentation
Unwrapthe iris
Featureextraction
Iriscode
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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)
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Iris code
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Achieving orientation invariance
Consequences: statistically, seeking the best match assuming thatrotated codes behave as independence codes provides a newinter-class distribution with reduced mean.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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).
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
Orientation distribution
To avoid excessive rotations that would harm the recognition a statisticaltest was carried to evaluate the distribution of shifts along the database.
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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′)
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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′)
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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:
Introduction to biometrics Iris anatomy Encoding an iris Matching codes Experimental results Conclusions
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.