The document examines the stability of iris recognition over a short period of time. It discusses how iris recognition works and why the iris is considered unique and stable over time. The research presented in the document analyzed iris image data collected over four weekly visits. The results showed no statistically significant difference in iris matching scores between the different visits, suggesting the iris is stable over a short time period. This supports the idea that the iris can be used for biometric identification applications that require stability over time.

1. 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

2. 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?

3. • 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?

4. • 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

5. “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?

6. •Remains with person barring catastrophic physical
damage
•Must be unique person-to-person
•Found in most of population
•Offers a stable environment
WHY BIOMETRICS?

7. •Physiological – Measurement of body parts
• Fingerprint
• Iris
• Face
• Palm vein
•Behavioral – Measurement of actions of user
• Keystroke
• Gait
• Signature
PHYSIOLOGICAL VS BEHAVORIAL
BIOMETRICS

8. • 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?

9. • 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?

10. 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]

11. •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

12. •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?

13. 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

14. • 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

15. • Research determining iris stability over time
• Data collected weekly, over four years [4]
• Data collected bi-annually
• Same result [5]
STABILITY RESEARCH

16. •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

17. • 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

18. •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

19. •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

20. • 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

21. •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

22. • 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

23. •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
.

24. •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

25. •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

26. • 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

27. •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

28. •Created by O’Connor [12]
•Used to calculate the stability for each
individual from one menagerie level to another
STABILITY SCORE INDEX

29. • 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

30. •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

31. • 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

32. • Images were matched using Neurotechnology’s
Megamatcher 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

33. •Yager and Dunstone menageries were created
for the dataruns
•Used because it visually compares the
genuine and impostor scores
MENAGERIE EVALUATIONS

34. •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)

35. • Images scored using SSI
• SSI is the Euclidean distance between two
points in menagerie
DATA ANALYSIS

36. RESULTS

37. VISIT 5 AGE GROUPS

38. VISIT 5 GENDER

39. VISIT 5 – SELF DISCLOSED ETHNICITY

40. • 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

41. 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

42. 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

43. 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

44. 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

45. 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

46. •Restating the hypothesis
•Results summarized
•Reviewing stability of the iris
•Contribution to the field
•Future work
CONCLUSIONS

47. In a ten-minute enrollment period do the iris
match scores prove to be statistically stable?
RESTATING THE HYPOTHESIS

48. •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

49. •These results show that the iris is stable over
time
REVIEWING STABILITY OF THE IRIS

50. •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

51. •Testing the stability of the iris over longer
periods of time (days, weeks, etc.)
•Continued replication with similar data
FUTURE WORK

52. [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.
BIBLIOGRAPHY