The document investigates the stability of the iris as a biometric characteristic over time, focusing on data collection conducted over a year. It evaluates whether iris stability scores change within short time frames and analyzes factors affecting its stability, including age and visual aids. The results indicate that the iris remains stable during brief collection periods, supporting earlier theories by Daugman.

1. EXAMINING INTRA-VISIT
IRIS STABILITY (VISIT 1)
Bo Brown, Jing Guan, Vince Sipocz, Aidan Chamberlain, Brandon
Cox, Preston Flint, Eric Hollensbe, Brandon Krieg, David Manfred,
Zack Tauer, Kevin Chan, Steve Elliott, and Ben Petry

2. • Automatic recognition of individuals based on their
distinguishing biological and behavioral features. [1]
• Types:
– Face, voice, fingerprint, and iris.
– Can be physiological and behavioral.
BIOMETRICS

3. IRIS ASSUMPTIONS
• Unique, stable over time [2]
• Recognition is a faster and less intrusive
method for biometrics
• Performance could be attributed to other
issues, not the biological stability of the iris
• Pupil dilation could be affected by a
number of factors

4. •The iris is the colored portion of the eye. [3]
•The outer bounds are defined by the white
sclera.
•The inner bounds are defined by the black
pupil.
STUCTURE OF THE EYE

5. PROBLEM STATEMENT
• Is the iris stable over time?
• Specifically:
• Does the stability score index change across four
groupings of three images taken in succession in
one visit within a day?

6. • 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

7. •Aging definition [4]
• To make old; cause to grow or seem old
• To bring to maturity or a state fit for use
AGING

8. • 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

9. • Definition: The tendency to remain accurate over time [3]
• Research focus: Examining stability of the iris over time
• Action plan:
• Reviewed time, and issues on the dynamics of time
• Examined template aging vs. biological aging
• Developed a methodology
• Completed the data analysis
• Results
STABILITY

10. • 𝑺. 𝑺. 𝑰𝒊 =
𝒙 𝒊 𝟐
− 𝒙 𝒊 𝟏
𝟐
+ 𝒚 𝒊 𝟐
−𝒚 𝒊 𝟏
𝟐
𝒙 𝒎𝒂𝒙− 𝒙 𝒎𝒊𝒏
𝟐 + 𝒚 𝒎𝒂𝒙−𝒚 𝒎𝒊𝒏
𝟐
[6]
STABILITY SCORE INDEX

11. •Debate in the research community about the
stability of the iris – and the iris template.
•Although the iris is stable over time [4], 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

12. • Data were collected as part of a multimodal study,
at the International Center for Biometric Research
• Data were collected in a controlled lab
environment
• Data were subject to ground truth, i.e. – checked
for errors and consistency
DATA

13. RESULTS

14. VISIT 1 AGE GROUPS

15. VISIT 1 GENDER

16. VISIT 1 – SELF DISCLOSED ETHNICITY

17. VISIT 1 N H DF P
Group 1 60 0.08 2 0.960
Group 2 60 0.89 2 0.642
Group 3 60 1.70 2 0.428
Group 4 60 0.45 2 0.800
RESULTS
There was not a statistically significant difference between the median of
the groupings, as indicated in the summary table. For this data, we can
conclude that the iris is stable in this visit.

18. •The results show that the iris is stable over a
collection period of less then fifteen minutes,
as theorized by Daugman [1][4].
CONTRIBUTION TO THE FIELD

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

20. [1] History of Biometrics. (n.d.). Retrieved February 20, 2015, from
http://www.biometricupdate.com/201501/history-of-biometrics
[2] Structure of the Eye, http://www.uofmhealth.org/health-library/tp9807
[3] Daugman, J. (2004). How iris recognition works. Circuits and Systems for Video
Technology, IEEE Transactions on, 14(1), 21-30.
[4] 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
[5] 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.
[6] O'Connor, K. J. (2013). Examination of stability in fingerprint recognition across force
levels, MS. Thesis, Purdue University, West Lafayette, IN.
BIBLIOGRAPHY