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. 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 1NHDFP Group 1600.0820.960 Group 2600.8920.642 Group 3601.7020.428 Group 4600.4520.800 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 http://www.biometricupdate.com/201501/history-of-biometrics [2] Structure of the Eye, http://www.uofmhealth.org/health-library/tp9807http://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