EXAMINING INTRA-VISIT

IRIS STABILITY (VISIT 4)Michael Boyle, Kyle Hurd, Mike Lozevski, Matthew Maclennan, Shreya Mohandas, Deep

Patel, Chloe Pina, Alex Wolowiecki, Taylar Worrell, Kevin Chan, Steve Elliott, Ben Petry

WHAT IS BIOMETRICS?

•Biometrics can be defined as “the automated

recognition of an individual based on

physiological or behavioral characteristics for

the purposes of identification and/or

verification” [1].

WHY IS BIOMETRICS IMPORTANT?• Security

• Banks

• Schools

• Airports

• Corporations

• Solve inherent problem to token/password authentication

• Unique identification that cannot be stolen, lost, or forged

BIOMETRICS

• Can be either physiological or behavioral

• Examples

• Fingerprint

• Hand Geometry

• Facial Recognition

• Retina Scanners

• Iris Recognition

• Gait

IRIS RECOGNITION

The benefits to implementing iris recognition as a primary biometric validator and identifier:

• Unique to every person

• Unobtrusive

• Fast

• Well protected from external forces

• More resistant to change

STABILITY

• It has been assumed that iris matching scores

will remain relatively stable over time [2]

•The research question is whether the iris is

stable over a single visit of 10 or fewer minutes

WHAT IS AN IRIS?

•The iris is the colored part of the eye in

between the pupil and the sclera [3]

•𝑺. 𝑺. 𝑰𝒊 =𝒙𝒊𝟐− 𝒙𝒊𝟏

𝟐+ 𝒚𝒊𝟐−𝒚𝒊𝟏

𝟐

𝒙𝒎𝒂𝒙− 𝒙𝒎𝒊𝒏𝟐 + 𝒚𝒎𝒂𝒙−𝒚𝒎𝒊𝒏

𝟐[12]

STABILITY SCORE INDEX

AGING

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

• Aging is one concern for implementing iris recognition

• Research has shown that as an individual ages, they are less likely to match with the template they originally enrolled with

• This phenomenon is known as template aging

RESEARCH QUESTION

•This research will assess whether or not the

template aging affect, as well as a possible iris

aging, are an influential factor in the overall

change in the Stability Score Index over a

period of 10 or fewer minutes.

•ROC

•DET

•Zoo menagerie

WAYS OF ANALYZING BIOMETRIC

PERFORMANCE

• Iris recognition results can change due to

many variables

• Iris stability describes to what degree an iris’s

score can vary

•Used for more accurate individual acceptance

tolerances

DEFINE STABILITY OF THE IRIS

RESULTS

VISIT 1 AGE GROUPS

VISIT 1 GENDER

VISIT 1 – SELF DISCLOSED ETHNICITY

•After running the different groupings of data

sets through Minitab, using graphical

summaries and Kruskal-Wallis Tests.

ANALYSIS

• Alpha level of 0.05 is less than the p value of 0.841, fail to reject the null hypothesis. There wasn’t a statistical difference between the SID scores (H = 0.35, p = 0.814), with a mean of 0.14973. This result means that there is stability in the iris.

GROUP 1

• Alpha level of 0.05 is less than the p value of 0.663, fail to reject the null hypothesis. There wasn’t a statistical difference between the SID scores (H = 0.82, p = 0.663), with a mean of 0.14683.This result means that there is stability in the iris.

GROUP 2

• Alpha level of 0.05 is less than the p value of 0.343, fail to reject the null hypothesis. There wasn’t a statistical difference between the SID scores (H = 2.14, p = 0.343), with a mean of 0.14973. This result means that there is stability in the iris.

GROUP 3

• Alpha level of 0.05 is less than the p value of 0.569, fail to reject the null hypothesis. There wasn’t a statistical difference between the SID scores (H = 1.13, p = 0.569), with a mean of 0.15878. This result means that there is stability in the iris.

GROUP 4

• After running the different groupings of data sets

through the Minitab software, using graphical

summaries and Kruskal-Wallis Tests, there was no

significant difference in the individual SSI scores as

well as a difference between each of the groupings’

significance values.

ANALYSIS

VISIT 1 N H DF P

Group 1 60 0.35 2 0.814

Group 2 60 0.82 2 0.663

Group 3 60 2.14 2 0.343

Group 4 60 1.13 2 0.569

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.

•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.

CONCLUSION

•With these finding we would want to extend the period of time that the iris’ are tested over to prove they continue to be stable. Along with this we would like to see if the stability of the iris starts to decrease over extended period of time.

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.

BIBLIOGRAPHY