In this research, intra-visit match score stability was examined for the human iris. Scores were found to be statistically stable in this short time frame.

1. Patrick Herrmann, Kautilya Madhav, Catherine Muturi, Jack
Rosati, Curtis Rose, Jonathan Ruggaard, Ryan Rumble, Kyle
Senteney, Ben Petry, Steve Elliott, and Kevin Chan
EXAMINING INTRA-VISIT
IRIS STABILITY (VISIT 6)

2. • How to Identify a Person
• Biometrics
• What is it?
• Types?
• Why Care?
• Iris Recognition
• What is it?
• How Recognition Works
• Stability
• Other research
• Research Question
INTRODUCTION OVERVIEW

3. •Identity can be verified in three ways:
•What someone knows: Secret Knowledge
•What someone has: Token
•What someone is: Biometrics
HOW TO IDENTIFY A PERSON

4. •Biometrics: “a measurable, physical
characteristic or biological characteristic used
to recognize the identity or verify these claimed
identity of an enrollee” [1]
WHAT IS BIOMETRICS?

5. •Large scale
•Not easily stolen or imitated
•Difficult to manipulate
ADVANTAGES OF BIOMETRICS

6. BIOMETRICS
• Physiological
• Hand geometry
• Fingerprint
• Iris Recognition
• Behavioral
• Signature
• Keystroke
• Voice

7. •This study will examine stability of match
scores from samples taken within a time frame
of 10 minutes or less.
RESEARCH QUESTION

8. •Controls the amount of light reaching the retina
•Captured using near infrared light
IRIS FUNCTION

9. •Depending on time window of sample
collection (10 min)
•IREX VI found stability [5]
•Notre Dame study found evidence of aging [6]
TIME CHANGE

10. •“Iris remains stable over time” [2]
•“Does not remain stable over time” [3]
•“Performance scores affected by time
separation” [4]
IRIS AGING / STABILITY RESEARCH

11. EFFECT ON THE EYE
Short Term
•Dilation
•Lighting
•Environment
•Glare
Long Term
•Muscular Degeneration
•Damage to iris
•Cataracts
•Disease

12. •Consistency between samples of the individuals
DEFINE STABILITY OF THE IRIS
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
[7]

13. •Established in 2013 by O’Connor [7]
•Initial work in fingerprints, but research
continues in iris and face
•Another way of examining the performance of
a user
STABILITY SCORE INDEX

14. METHODOLOGY

15. • Data used was from a 2012 ICBR data collection captured at
Purdue University
• Examined data runs in depth, created groupings and created
data sets of each grouping
• Used Megamatcher to analyze the data runs
• Stability score index created for each subject
OVERVIEW

16. • 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

17. • Identify any error for each subject and iris from data runs
• Subjects with incorrect number of images, further investigated
• If the number of subjects images were less than required
amount of images then they were eliminated
• The first data run was completely eliminated due to lack of
images
EXAMINED DATA RUNS IN DEPTH

18. • Subjects were narrowed down to only those that met testing
requirements
• These subjects were then pooled into new datasets
• This included locator number, subject ID, and modality subtype
EXPORTING REQUIRED SUBJECTS

19. • Newly created data sets were then arranged into grouping based on
the subject and left or right iris
• Each subject has 4 groupings of 6 images per group, the images
were organized in order of three consecutive lefts and then three
consecutive rights
CREATED GROUPING FOR EACH IRIS AND
EACH VISIT

20. • Each grouping was put into its own data set
• Group 1 of all subjects were combined into one data set
• Done for all four groupings
SPLIT GROUPS INTO SEPARATE DATA RUNS

21. • The data runs were then processed by Megamatcher and
exhaustively matched against all other images
• Megamatcher then outputs a genuine and impostor score for each
subject
MEGAMATCHER USED TO ANALYZE DATA

22. • Stability score index (SSI) is made possible by taking the Euclidian
distance of an individual subject which determines the change in
location within the menagerie across two data runs
• The SSI ranges from 0-1, zero being the most stable score, it
allows for stability of subjects to be determined and quantified
CREATION OF STABILITY SCORE INDEX

23. RESULTS

24. VISIT 6 AGE GROUPS

25. VISIT 6 GENDER

26. VISIT 6 – SELF DISCLOSED ETHNICITY

27. •Grouping 1-V6 (1-2, 1-3, 1-4)
•Grouping 2-V6 (2-1, 2-3, 2-4)
•Grouping 3-V6 (3-1, 3-2, 3-4)
•Grouping 4-V6 (4-1, 4-2, 4-3)
DATA ANALYSIS

28. GROUPING 1 – V6 ANALYSIS
There was not a statistically significant
difference between the median stability scores
between different groupings (H(2) = 0.05, p =
0.976), with a mean rank of 91.7 for 1-2, 89.9 for
1-3, and 89.9 for 1-4.

29. GROUPING 2 – V6 ANALYSIS
There was a statistically significant difference
between the median stability scores between
different groupings (H(2) = 6.24, p = 0.044), with
a mean rank of 104.2 for 2-1, 84.3 for 2-3, and
83.0 for 2-4.

30. GROUPING 3 – V6 ANALYSIS
There was not a statistically significant
difference between the median stability scores
between different groupings (H(2) = 3.83, p =
0.148), with a mean rank of 101.0 for 3-1, 83.3
for 3-2, and 87.2 for 3-4.

31. GROUPING 4 – V6 ANALYSIS
There was not a statistically significant
difference between the median stability scores
between different groupings (H(2) = 5.77, p =
0.056), with a mean rank of 103.3 for 4-1, 81.3
for 4-2, and 87.0 for 4-1.

32. VISIT 1 N H DF P
Group 1 60 0.05 2 0.976
Group 2 60 6.24 2 0.044
Group 3 60 3.83 2 0.148
Group 4 60 5.77 2 0.056
RESULTS

33. CONCLUSIONS

34. •H0: The median stability scores are equal
•Ha: The median stability scores are not equal
•α = 0.05
HYPOTHESIS

35. •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, even though the second
grouping shows significant difference.
RESULTS SUMMARY

36. •More research to be conducted to validate the
stability of the iris over a longer period of time
(weeks, months, years)
•Re-examine datasets that rejected the null
hypothesis
FUTURE WORK

37. • [1] Association of Biometrics, 1999, p. 2
• [2] Daugman, J. (2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 21–30.
doi:10.1109/TCSVT.2003.818350
• [3] Baker, S. E., Bowyer, K. W., Flynn, P. J., & Phillips, P. J. (2013). Template Aging in Iris Biometrics : Evidence of Increased False Reject
Rate in ICE 2006. In Handbook of Iris Recognition, 205–218, London: Springer.
• [4] Tome-Gonzalez, P., Alonso-Fernandez, F., & Ortega-Garcia, J. (2008). On the Effects of Time Variability in Iris Recognition. Biometrics:
Theory, Applications, and Systems, 2008. 2nd IEEE International Conference, 1–6.
• [5] Grother, P., Matey, J. R., Tabassi, E., Quinn, G. W., & Chumakov, M. (2013). IREX VI. Temporal Stability of Iris Recognition Accuracy. NIST
Interagency Report, 7948, 1-3.
• [6] 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.
• [7] O’Connor, K. J. (2013). Examination of stability in fingerprint recognition across force levels. Purdue University, West Lafayette, Indiana.
REFERENCES