EXAMINING INTRA-VISIT

IRIS STABILITY (VISIT 5)Elizabeth Bartley, Grant Eibling, Tim Kovacic, Tomas

Kratka, Dustin Phillips, Cameron Posey, Shivaadas

Silvadas, Ben Petry, Steve Elliott, Kevin Chan

OVERVIEW - INTRODUCTION

• How Do We Identify?• Verification vs

Identification• What is Biometrics?• Physiological vs

Behavioral• Why Iris Recognition?• Stucture of the Eye

• Structure of the Iris• What is Stability?• Iris Stability Over Time

(Aging)• Template vs Iris Aging• Stability Research• So What?

• What you have – Token

• Drivers license, passport, Social Security card

• What you know – Knowledge

• Password, keyword, PIN

• What you are – Biometrics

• Iris, Fingerprint, Face, and Voice

HOW DO WE IDENTIFY?

• Verification

• I am who I say I am

• Matching an individual to a stated identity

• 1:1

• Identification

• I am not who I say I am not

• Matching an individual to all templates in a database

• 1:N

VERIFICATION VS IDENTIFICATION

“Biometrics is defined as any automatically

measurable, robust, and distinctive physical

characteristic or personal trait that can be used to

identify an individual or verify the claimed identity of

an individual” [1]

WHAT IS BIOMETRICS?

• Remains with person barring catastrophic physical damage

• Must be unique person-to-person

• Found in most of population

• Offers a stable environment

WHY BIOMETRICS?

• Physiological – Measurement of body parts

• Fingerprint

• Iris

• Face

• Palm vein

• Behavioral – Measurement of actions of user

• Keystroke

• Gait

• Signature

PHYSIOLOGICAL VS BEHAVORIAL

BIOMETRICS

• Daugman states that the iris is an, “internal

(yet externally visible) organ of the eye, the

iris is well protected from the environment

and stable over time” [2]

WHY IRIS RECOGNITION?

• Capture from a distance – no interaction, on the move

• Internal, yet externally visible

• The details of the iris, such as striations, patterns, rings, and freckles make each one completely unique from any other in the world.

• An individual has a 1:1078 chance of their iris matching completely to any other iris in the world, even their own opposite iris [2]

WHY IRIS RECOGNITION?

STUCTURE OF THE EYE

• The iris is the colored

portion of the eye

• Its’ outer bounds are defined

by the white sclera

• Its’ inner bounds are defined

by the black pupil

[3]

•The iris has a plethora of variation and

complex structures unique to each individual

•This makes iris recognition particularly good

for recognition

STRUCTURE OF THE IRIS

•The resiliency to variation of a biometric

modality over a determined time interval or the

resiliency to change given certain

environmental factors

WHAT IS STABILITY?

IRIS STABILITY OVER TIME (AGING)• There is debate as to whether or not the iris changes over

time due to aging

• Iris aging is a definitive change in the iris texture pattern

due to human aging

• Evidence has shown that there is no change in the iris over

time over time due to aging

• A template aging effect occurs when the quality of

the match between an enrolled biometric sample

and a sample to be verified degrade with the

increased elapsed time between two samples.

• Algorithm to find a match finds a difference causing

the match scores to decrease.

• Iris aging is a definite change in the iris texture

pattern that occurs from human aging.

TEMPLATE VS IRIS AGING

• Research determining iris stability over time

• Data collected weekly, over four years [4]

• Data collected bi-annually

• Same result [5]

STABILITY RESEARCH

• Given prior research, there are debates about how stable an iris is over time

• How long is the iris stable for?

• This research will determine if, during a ten-minute enrollment period do the iris match scores prove to be statistically stable?

RESEARCH QUESTIONS

• How to Identify a Person

• Verification

• Identification

• Biometric Authentication

• History of Biometrics

• What is the Iris?

• Why is the Iris Unique?

• Iris Recognition History

• How Iris Recognition Works

• Stability of the Iris

• Ways of Analyzing Biometric

Consistency

• ROC/DET Curves

• Zoo Menagerie

• Advantages/Disadvantages of the

Zoo Menagerie

• Stability Score Index (SSI)

LITERATURE REVIEW - OVERVIEW

• There are three ways to identify a person:

• Knowledge (passwords, PINs, etc.)

• Tokens (credit card, student ID card, etc.)

• Biometrics (fingerprint, iris, etc.)

• The challenge now lies in making biometrics a viable

way to provide security for a person.

HOW TO IDENTIFY A PERSON

• Biometrics is a way of uniquely identifying a person through their physical and behavioral traits

• Physical traits include fingerprints, the iris, the face, etc.

• Behavioral traits include speech, signature, etc.

• Because it relies on these characteristics, biometrics reduces the chances of fraud.

BIOMETRIC AUTHENTICATION

• In ancient cultures, like China, Babylon, and Egypt, people were using

biometrics to identify important documents and mark their land.

• By the late 1800’s, police departments started using fingerprints as a

method of identification.

• Concepts such as face and iris recognition came about in the late

1900’s, which gave us more options for security at places like airports

and government facilities. [6]

HISTORY OF BIOMETRICS

•The concept of iris recognition has developed

since Adler proposed an image of the iris as a

means of identification [8]

• John Daugman developed the first iris

recognition algorithm [9]

IRIS RECOGNITION HISTORY

• Iris recognition requires a person’s iris to be matched against a

template provided when the person enrolled in the system.

• The iris must first be segmented.

• Involves the use of edge contract techniques to eliminate “irrelevant” information like

the pupil, sclera, and eyelid.

• Next, the iris is normalized

• Translates the image into a rectangular image with fixed dimensions.

• Recognition systems will compare a person’s iris code against a

template using the Hamming Distance algorithm.

HOW IRIS RECOGNITION WORKS

•Although the iris is stable over time [10], the iris template can change.

• Changes that can affect stability include, but are not limited to:

•The presence of visual aids (like glasses or contacts)

•The occlusion of the iris caused by the eyelids

STABILITY OF THE IRIS

.

•To analyze the performance of the iris, we can use the following tools:

• Reciever Operating Characteristic (ROC) Curve

• Detection Error Trade-off (DET) Curve

• Zoo menagerie

WAYS OF ANALYZING BIOMETRIC

CONSISTENCY

•Receiver Operating Characteristics (ROC) curves

• Display the tradeoff between exactly confirming a user to a template against analyzing the wrong person

•Detection Error Trade-Off (DET) curves

• Display the trade-off of the false accept rate and false reject rate.

ROC/DET CURVES

• The plots that can provide an individual’s performance with respect to others

• A collection of different animals that are used to describe a subject’s matching

tendencies, which include:

• Sheep: the default population; they match well with themselves and poorly

against others.

• Goats: difficult to recognize; they have low match scores against themselves.

• Lambs: easiest to imitate; they match well with others which can lead to false

accepts.

• Wolves: able to imitate others easily [11]

ZOO MENAGERIE

•Advantages

• Helps researchers identify the biggest threats to biometric systems and how they can protect these systems from creating false matches.

• Identify mistakes in a system algorithm or data capturing

•Disadvantages

• Classifications depend on the calibration of the iris recognition system.

• Dependent on the algorithm used to calculate match scores and the iris used for comparisons

ADVANTAGES/DISADVANTAGES OF

THE ZOO MENAGERIE

•Created by O’Connor [12]

•Used to calculate the stability for each individual from one menagerie level to another

STABILITY SCORE INDEX

• Extract dataruns from the image database housed at ICBR

• Identify Errors

• Clean data

• Exporting required subjects from the database

• Create groupings for each iris for each visit

• Split groupings into their own dataruns

METHODOLOGY - OVERVIEW

• Number the images per iris per subject

• Examined for number of images then made determination:

• To many images, check why: > 25

• Usable number: 12 > n > 25

• Unusable number of images: < 12

IDENTIFYING ERRORS

• Each cleaned and segmented file was split into files for each grouping per visit and given a DatarunID.

• These files were then uploaded into the Database which used the DatarunID and LocatorNum to create new dataruns which can be exported to Dataset and ran through Megamatcher.

• Number of LocatorNum per file = 180.

SPLIT GROUPINGS INTO THEIR OWN

DATARUNS

• Images were matched using Neurotechnology’sMegamatcher 4.0

• Output results as genuine and impostor scores

• Genuine scores indicate a proven match to a given template

• Impostor scores indicate a non-match to a given template

IMAGE MATCHING

•Yager and Dunstone menageries were created

for the dataruns

•Used because it visually compares the

genuine and impostor scores

MENAGERIE EVALUATIONS

• Calculates the distance between any two points of a zoo menagerie using genuine and imposter scores

• Used to calculate the difference between the data runs

• 0(stable) -1(unstable)

STABILITY SCORE INDEX (SSI)

• Images scored using SSI

• SSI is the Euclidean distance between two points in menagerie

DATA ANALYSIS

RESULTS

VISIT 5 AGE GROUPS

VISIT 5 GENDER

VISIT 5 – SELF DISCLOSED ETHNICITY

• Data collection began on 11 June 2010 and lasted for 1 year and 2 days (2010-06-11Z/P1Y0M0W2D).

• The time scope of interest for this report is in the day range.

• The collection period of interest for this analysis began on 11 April 2013 and lasted for four weeks and 1 day (2013-04-11Z/P0Y0M4W1D).

COLLECTION PERIOD

VISIT 1 N H DF P

Group 1 60 4.69 2 0.096

Group 2 60 4.39 2 0.111

Group 3 60 5.02 2 0.081

Group 4 60 2.26 2 0.324

RESULTS

GROUPING 1 - ANALYSIS

There was not a statistical difference between

the average score of each grouping (H(2) =

4.69, P = 0.096) with a mean score of 0.12999

for grouping 1-2, 0.11809 for grouping 1-3,

0.7645 for grouping 1-4

There was not a statistical difference between

the average score of each grouping (H(2) =

4.39, P = 0.111) with a mean score of 0.12999

for grouping 2-1, 0.8321 for grouping 2-3,

0.13582 for grouping 2-4

GROUPING 2 - ANALYSIS

GROUP 3 - ANALYSIS

There was not a statistical difference between

the average score of each grouping (H(2) =

5.02, P = 0.081) with a mean score of 0.11809

for grouping 3-1, 0.8321 for grouping 3-2,

0.11038 for grouping 3-4

GROUP 4 - ANALYSIS

There was not a statistical difference between

the average score of each grouping (H(2) =

2.26, P = 0.324) with a mean score of 0.7645 for

grouping 4-1, 0.13582 for grouping 4-2, 0.11038

for grouping 4-3

• Restating the hypothesis

• Results summarized

• Reviewing stability of the iris

• Contribution to the field

• Future work

CONCLUSIONS

In a ten-minute enrollment period do the iris

match scores prove to be statistically stable?

RESTATING THE HYPOTHESIS

•There was no statistically significant difference between the four data runs, as shown in the results section.

• All data runs have a p-value greater than the alpha of 0.05, which is why we fail to reject our null hypothesis

RESULTS SUMMARIZED

•These results show that the iris is stable over

time

REVIEWING STABILITY OF THE IRIS

•The results show that the iris is stable over a

short period of time (one visit)

•This can be later expanded to see if the iris is

stable over longer periods of time

CONTRIBUTION TO THE FIELD

•Testing the stability of the iris over longer

periods of time (days, weeks, etc.)

•Continued replication with similar data

FUTURE WORK

[1] Woodward Jr, J. D., Horn, C., Gatune, J., & Thomas, A. (2003). Biometrics: A look at facial recognition. RAND Corp, Santa Monica, CA.

[2] Daugman, J. (2004). How iris recognition works. Circuits and Systems for Video Technology, IEEE Transactions on, 14(1), 21-30.

[3] Structure of the Eye, http://www.uofmhealth.org/health-library/tp9807

[4] Baker, S. E., Bowyer, K. W., & Flynn, P. J. (2009). Empirical evidence for correct iris match score degradation with increased time-lapse between gallery and probe matches. In Advances in Biometrics (pp. 1170-1179). Springer Berlin Heidelberg.

[5] Tome-Gonzalez, P., Alonso-Fernandez, F., & Ortega-Garcia, J. (2008, September). On the effects of time variability in iris recognition. In Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on (pp. 1-6). IEEE.

[6] History of Biometrics. (n.d.). Retrieved February 20, 2015, from http://www.biometricupdate.com/201501/history-of-biometrics

[7] Iris ID - Iris Recognition Technology : Iris Recognition Technology. (n.d.). Retrieved February 20, 2015, from http://www.irisid.com/irisrecognitiontechnology

[8] Adler, F.H., Physiology of the Eye (Chapter VI, page 143), Mosby (1953)

[9] Daugman, J. (2004). How iris recognition works. Circuits and Systems for Video Technology, IEEE Transactions on, 14(1), 21-30.

[10] Daugman, J. (2006). Probing the uniqueness and randomness of IrisCodes: Results from 200 billion iris pair comparisons. Proceedings of the IEEE, 94(11), 1927-1935

[11] Doddington, G., Liggett, W., Martin, A., Przybocki, M., & Reynolds, D. (1998, November). Sheep, goats, lambs and wolves: an analysis of individual differences in speaker recognition performance. In the International Conference on Spoken Language Processing (ICSLP), Sydney.

[12] O'Connor, K. J. (2013). Examination of stability in fingerprint recognition across force levels, MS. Thesis, Purdue University, West Lafayette, IN.

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