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)

• 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

• 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

•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?

•Large scale

•Not easily stolen or imitated

•Difficult to manipulate

ADVANTAGES OF BIOMETRICS

BIOMETRICS

• Physiological

• Hand geometry

• Fingerprint

• Iris Recognition

• Behavioral

• Signature

• Keystroke

• Voice

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

RESEARCH QUESTION

•Controls the amount of light reaching the retina

•Captured using near infrared light

IRIS FUNCTION

•Depending on time window of sample collection (10 min)

• IREX VI found stability [5]

•Notre Dame study found evidence of aging [6]

TIME CHANGE

• “Iris remains stable over time” [2]

• “Does not remain stable over time” [3]

• “Performance scores affected by time separation” [4]

IRIS AGING / STABILITY RESEARCH

EFFECT ON THE EYE

Short Term

• Dilation

• Lighting

• Environment

• Glare

Long Term

• Muscular Degeneration

• Damage to iris

• Cataracts

• Disease

•Consistency between samples of the individuals

DEFINE STABILITY OF THE IRIS

[7]

•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

METHODOLOGY

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

RESULTS

VISIT 6 AGE GROUPS

VISIT 6 GENDER

VISIT 6 – SELF DISCLOSED ETHNICITY

•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

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.

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.

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.

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.

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

CONCLUSIONS

•H0: The median stability scores are equal

•Ha: The median stability scores are not equal

•α = 0.05

HYPOTHESIS

•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

•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

• [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