Cameron Fevig, Tyler Heister, Aayush Jhunjhnuwala,

Sean Mince, Ayushi Pradhan, Mark Shimala, Kevin

Jones, Ben Petry, Steve Elliott, and Kevin Chan

EXAMINING INTRA-VISIT

IRIS STABILITY (VISIT 3)

•Replication

•Biometrics

• Iris Recognition

•Research Question

INTRODUCTION

•Three main ways:

• Tokens – Drivers license, passport, ID card

• Secret Knowledge – PIN, password

• 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]

BIOMETRICS – WHAT IS IT?

•There are many instance where we need to

identify an individual before allowing them to

gain access to something.

•For example – confirming identity before

crossing a country border

BIOMETRICS – WHY CARE?

• Physiological features:

• Face

• Fingerprint

• Retinas

• Iris

• Behavioral traits:

• Voice

• Signature

• Keystroke Dynamics

BIOMETRICS – MODALITIES

•The iris is the colored part of the eye, located

between white sclera and black pupil

•Acts as the muscle that controls the light levels

allowed inside the eye [2]

WHAT IS THE IRIS?

• The chances of 2 people having matching iris’ is about 1 in 10^78

• It is an internal organ that is well protected and externally visible

• Can be captured from a distance while subjects are moving

• Can be quickly matched to templates stored in a database [2]

IRIS RECOGNITION – WHY USE IT?

•Template aging refers to technical deterioration

of saved iris template

• Iris aging refers to actual changing of physical

iris over time

TEMPLATE VS IRIS AGING

• “The iris is well protected from the environment

and stable over time” [2]

STABILITY – DAUGMAN

•Since Daugman states that the iris is stable,

aging should not affect iris recognition

performance [2]

STABILITY - PERFORMANCE

•Does a study of iris performance scores exhibit

statistical stability when examing a time period

of 10 or fewer minutes?

RESEARCH QUESTION

• Type or class of biometric system

• Any biological or behavioral characteristic that can be measured.

• Face

• Fingerprint

• Hand Geometry

• Keystroke Dynamics

• Etc.

MODALITIES

• Arching ligaments

• Furrows

• Ridges

• Crypts

• Rings

• Corona

• Freckles

• Zigzag collarette [2]

IRIS CHARACTERISTICS

• Formula which creates an Euclidian distance between any two points of a zoo menagerie

𝑆𝑆𝐼𝑖 =

(𝑥𝑖2 − 𝑥𝑖1)2 + (𝑦𝑖2 − 𝑦𝑖1)

2

(𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛)2 + (𝑦𝑚𝑎𝑥 − 𝑦𝑚𝑖𝑛)

2

STABILITY SCORE INDEX (SSI) [3]

•Examining images collected from different

visits from a four year study concluded that the

hamming distance varies significantly over a

larger period of time compared to a shorter

period. [4]

OTHER WORKS

• Datarun collection

• Grouping creation

• SID assignment

• Score generation

• Menagerie creation

• SSI calculation

METHODOLOGY

DATARUNS

•Collected dataruns

•Note: Subjects in dataruns were present for all

visitsDatarunID Visit Start Date End Date Days

821 1 6/11/2012 4/25/2013 318

822 2 3/29/2013 4/29/2013 31

823 3 4/11/2013 5/9/2013 48

824 4 4/22/2013 5/29/2013 37

825 5 4/26/2013 6/5/2013 40

826 6 5/6/2013 6/12/2013 37

827 7 5/14/2013 6/18/2013 35

828 8 5/28/2013 6/18/2013 21

EXAMINE DATARUNS IN DEPTH

• Identified any errors for each subject

• 821 - Different collection methodology from the other data runs and a much longer collection period.

• 822 - Many subjects post data cleaning resulted in the loss of several images. Therefore, these subjects did not have the 12 minimum required images. Cleaned data to remove subjects that did not have at least 12 images for one visit.

GROUPING CREATION FOR EACH IRIS,

EACH VISIT

•Created groupings for each iris for each visit

•Sorted images into groupings for first three left

and first three right images for the same

subject

GROUPINGS SPLIT INTO THEIR OWN

DATARUNS

•Split groupings into their own dataruns

•Took each grouping and split into individual

excel files

•Datasets were created for each grouping for

each visit

•Grouping 1 (1-2, 1-3, 1-4)

•Grouping 2 (2-1, 2-3, 2-4)

•Grouping 3 (3-1, 3-2, 3-4)

•Grouping 4 (4-1, 4-2, 4-3)

RESULTS – VISIT 3

The following information applies to the upcoming results:

H0 = the median stability scores are equal

Hα = the median stability scores are not equal

α = 0.05

RESULTS

VISIT 1 AGE GROUPS

VISIT 1 GENDER

VISIT 1 – SELF DISCLOSED ETHNICITY

•There was not a statistically significant

difference between the median of the

groupings (H(2) = 2.78, p = 0.249), with a

mean rank of 94.3 for grouping 1-2, 95.8 for

grouping 1-3, and 81.4 for grouping 1-4.

GROUPING 1 RESULTS

•There was not a statistically significant

difference between the median of the

groupings (H(2) = 1.28, p = 0.526), with a

mean rank of 95.2 for grouping 2-1, 91.7 for

grouping 2-3, and 84.6 for grouping 2-4.

GROUPING 2 RESULTS

•There was not a statistically significant

difference between the median of the

groupings (H(2) = 0.51, p = 0.774), with a

mean rank of 93.9 for grouping 3-1, 87.1 for

grouping 3-2, and 90.5 for grouping 3-4.

GROUPING 3 RESULTS

•There was not a statistically significant

difference between the median of the

groupings (H(2) = 2.28, p = 0.320), with a

mean rank of 86.9 for grouping 4-1, 85.9 for

grouping 4-2, and 98.8 for grouping 4-3.

GROUPING 4 RESULTS

VISIT 3 N H DF P

Group 1 60 2.78 2 0.249

Group 2 60 1.28 2 0.526

Group 3 60 0.51 2 0.774

Group 4 60 2.28 2 0.320

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 all of the groupings.

• The P-value was greater than the α- value of 0.5 for all the groupings, hence we fail to reject the null hypothesis which stated that the median stability scores are equal.

CONCLUSION

CONTRIBUTION AND FUTURE WORK

• Through this study we have been able to show that the Iris is a stable modality when images were collected within one visit

• There are many ways to expand on this research

• Replicate the research with different matcher and/or collection device to determine match/collection device effects on ‘aging’

• Do a similar study but with different collection periods e.g. months or years apart

[1] Association of Biometrics, 1999, p. 2

[2] J. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, Jan. 2004.

[3] O’Connor, K. J. (2013). Examination of stability in fingerprint recognition across force levels. Purdue University, West Lafayette, Indiana.

[4] S. P. Fenker, K. W. Bowyer, “Experimental Evidence of a Template Aging Effect in Iris Biometrics University of Notre Dame University of Notre Dame,” pp. 232–239, 2010.

REFERENCES