Apresentação - Iris Recognition

8/17/2019 Apresentação - Iris Recognition

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IRIS RECOGNITION

Igor Leonardo O. Bastos – [email protected]

Handbook of Biometrics – Chapter 4



Summary

Understanding an eye

A short history about iris recognition

Current state

The Method

Off-Axis Gaze Correction

Detecting and Excluding Eyelashes

Evaluation

References



UNDERSTANDING

AN EYE



Understanding an eye

How is called any part of an eye?

Fig 1 – Eye parts and its names



A SHORT HISTORY OF

IRIS RECOGNITION



A Short History of Iris Recognition

Iris was the target of studies since the ancient Egypt,

Chaldea and ancient Greece

Stone inscriptions, painted ceramic artifacts, writings

Commonly associated to the art of ‘Divination’

Fig 2 – Symbol of protection and royal power




Studies about iris remits to Hippocrates writings and

Imothep’s

Fig 3 – Hippocrates and Imhotep




Alphonse Bertillon iris as a distinguishing human

identifier

James Doggartcomplexity of iris patterns and

adequacy to serve in the same way of fingerprints(oneness)

Flom and Safir patent of Doggart’s concept but no

method to make it possible

Fig 4 – Alphonse Bertillon, Leonard Flom and Aran Safir



CURRENT STATE



Current State

Developed rapidly over the past 15 years Large number of active researchers in academy and

industry

Lots of people enrolled in iris recognition systems

Systems are usually designed for use in

identification-modeOne-to-many exhaustive search

Astonishing number of comparisons



Current State

Basic research into alternative methods continues

Scientific and engineering literature about iris

recognition grows monthly

Contributions from several university and industrial labs

around the world

Iris recognition systems employed by government

agencies

Project IRIS from UK to identify immigrants



Current State

Many iris recognition datasets are available

CASIA Iris Image Database (v4)

UPOL Iris Database

UBIRIS Database

ICE 2005 Database

BATH University Iris Database



THE METHOD



The Method

Finding the Iris is the first thing to be done

Inner and outer boundaries at pupil and sclera

Upper and lower eyelids

Superimposed eyelashes or reflections from the cornea oreyeglasses

Precision in assigning the true inner and outer

boundaries is a critical operation Innacuracy can cause different mappings of the iris pattern

in its extracted description



The Method

Important point iris is not an annulus

Inner and outer boundaries are usually not concentric

Pupil boundaries and outer iris boundaries are non-circular

Performance improved by relaxing bothassumptions

Methods for modelling boundaries whatever their shapes



The Method

Finding the correct countours can be difficult

Eyelids can occlude iris outer boundary

Reflections from illumination can occlude iris inner boundary

Both can be occluded by reflections from eyeglasses

It is necessary to fit flexible contours that can

tolerate interruptions

A constraint both boundaries are closed curves



The Method

Employment of Active Contours based on discrete

Fourier series expansions of the contour data

Selecting the number of frequency componentsallows control over the degree of smoothness that is

imposed

Truncating means applying a low-pass filtering over theboundary curvature data



The Method

Fig 5 – Snakes and corresponding maps

Snakes representing pupil and iris boundaries

Ideal snakes would be flat and straight



The Method

There is a tradeoff between simplicity of the model

and its precision

Number of terms used on the Fourier Series (M)

Empirically, the author found good approximations

for values M=17 (pupil) and M=5 (iris)



OFF-AXIS GAZE

CORRECTION




Model requires an on-axis image of the eye

Stop-and-stare interface

Correcting the projective deformation on the iriswhen its image is off-axis

The essence of the problem is estimating the anglesof gaze relative to the camera

Eye morphology is so variable that is unlikely that this factor

could support a robust estimation




Obvious alternative approach shape of pupil

Estimating the ellipticity of the pupil and orientation

of that ellipse would yield the gaze deviation

The author proposes something different Fourier-

based trigonometry




Fourier series expansions of the X- and Y-

coordinates of the pupil contains shape distortion

information related to the deviated gaze

If the pupil boundary is a circle, this method is

reduced to the previous one

Estimating these parameters gaze deviation to

be reversed by an affine transformation of the off-

axis image




Fig 6 – Off-Axis Gaze Correction



DETECTING AND

EXCLUDING EYELASHES



Detecting and excluding eyelashes

Eyelashes can occlude parts of the eye

Usually have random and complex shapes

Can be the strongest signals in iris images, in terms

of contrast and energy

They could dominate the image with spurious information

Fi 7 – E elashes occludin arts of the e e



Detecting and excluding eyelashes

Employment of statistical estimation methods that

depend essentially on determining whether the

distribution of iris pixels is multi-modal

If the lower tail of the iris pixel histogram supports

na hypothesis of multi-modal mixing, then an

appropriate threshold can be computed

Fi 8 – Histo ram and threshold com utation



EVALUATION



Evaluation

Comparison between IrisCodes (bit pair Bernoulli

trials)

Areas obscured by eyelids, eyelashes or byreflections from eyeglasses are processed and

prevented to influence the iris comparisons

IrisCodes keep the information about the phases

and are compared through bit-wise mask functions



Evaluation

Code related to each iris is ExclusiveOR’ed and

AND’ed to mask functions

Raw Hamming distance used to compared to irises:

The number of bits pairings available for

comparison is usually nearly a thousand



Evaluation

Restrictions related to the ‘behaviour’ of masks

Must take into account the amount of comparison

data that was available

A normalization is performed in order to improve

the confidence level score normalization



LARGE-SCALE

APPLICATIONS



Large-Scale applications

Score distribution for 200 Billion Different Iris Comparisons

Fig 9 – Hamming Distance of different irises



Large-Scale applications

Use of thresholds to compute the similarity of one iris to

another



OBRIGADO !



REFERENCES



References

1. John Daugman. Iris Recognition. In: Handbook of Biometrics,

Springer, USA (2008)

2. Enrique A. Velasco. Connections in Mathematical Analysis: The

Case of Fourier Series. In: American Mathematical Monthly,v.99, USA (1992).

3. Michael Kass, Andrew Witkin and Demetri Terzopoulos.

Snakes: Active Countor Models. In: International Journal ofComputer Vision, p. 321-331. (1998)

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Apresentação - Iris Recognition