Uba Anydiewu, Shane Bilinski, Luis Garcia, Lauren

Ragland, Debracca Thornton, Joe Tubesing, Kevin Chan,

Steve Elliott, and Ben Petry

EXAMINING INTRA-VISIT

IRIS STABILITY (VISIT 2)

Biometrics is defined as any automatically

measurable, robust, and distinctive physical

characteristic or personal trait that can be used to

identify an individual or verify the claimed identity of

an individual” [1]

WHAT IS BIOMETRICS?

• Physiological

• Face

• Iris

• Fingerprints

• Behavioral

• Keystroke

• Signature

• Gait

BIOMETRICS – PHYSIOLOGICAL /

BEHAVIORAL

• Improves

• Security

• Ease of use

• Reliability

BIOMETRICS

• Iris is the colored part of the eye in the center

of the sclera [2]

•The iris is unique and distinct from others [2]

IRIS – WHAT IS IT?

• Unique, stable over time [2]

• Recognition is a faster and less intrusive method for biometrics.

• Fingerprinting and hand geometry require physical contact.

• Stability affected by other sources. [2]

• Lighting

• Sickness

• Drug consumption

• Intoxication

IRIS

• The iris image is first captured.

• Localized for further feature extraction.

• Segmented into binary code.

• Iris is then compared to a template in the system to see if a match can be found [2].

HOW IRIS RECOGNITION WORKS

•The iris is assumed to remain stable over time.

•This means that the iris should not change its

unique characteristics [2].

STABILITY OF THE IRIS

•The iris should provide consistent genuine or

impostor scores.

•Stability is the resiliency to variation of a

biometric modality over a determined time

interval or the resiliency to change given

certain environmental factors [6].

STABILITY - PERFORMANCE

IRIS STABILITY OVER TIME (AGING)• There is debate as to whether or not the iris changes over

time due to aging.

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

due to human aging.

• Evidence has shown that there is no change in the iris over

time over time due to aging.

• What is it?

• Refers to changes in the enrolled template over time.

• How does it differ from iris aging?

• Iris aging = Human eye

• Template aging = Enrolled eye image

TEMPLATE AGING

• 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. [4]

TEMPLATE VS IRIS AGING

• Trend analysis – Practice of collecting information and attempting to spot a pattern.

• ROC Curve – Receiver operating characteristic, a graphical plot that illustrates the performance of a binary classifier system.

• DET Curve – Detection error tradeoff, a graphical plot of error rates for binary classification systems.

• Hamming Distance – found between two strings of equal length and determines how different they are.

WAYS OF ANALYZING BIOMETRIC

PERFORMANCE

• Genuine – Score when compared against a proven match

• Impostor – Score when compared against a proven non-

match

• FNMR – False Non-Match Rate

• ISO Standard (ISO 19795, clause 4.6.3)

• FMR – False Match Rate

• ISO Standard (ISO 19765, clause 4.6.4)

DEFINITIONS

• 6.3

• False non-match rate

• FNMR

• proportion of genuine attempt samples falsely declared not to match the template of the same characteristic from the same user supplying the sample

• Note 1 to entry: The measured/observed false non-match rate is distinct from the predicted/expected false non-match rate (the former may be used to estimate the latter).

• 6.4

• False match rate

• FMR

• Proportion of zero-effort impostor attempt samples falsely declared to match the compared non-self template

• Note 1 to entry: The measured/observed false match rate is distinct from the predicted/expected false match rate (the former may be used to estimate the latter).

DEFINITIONS: ISO 19795-4

RESULTS

VISIT 2 AGE GROUPS

VISIT 2 GENDER

VISIT 2 – SELF DISCLOSED ETHNICITY

VISIT 1 N H DF P

Group 1 60 1.32 2 0.517

Group 2 60 0.23 2 0.893

Group 3 60 0.14 2 0.932

Group 4 60 2.41 2 0.300

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.

CONCLUSIONS

•Future research

• Spans of 30 minutes or more

• Spans of 1 day or more

• Replicate with freshly collected data

FUTURE RESEARCH

• [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. (2009). How Iris Recognition Works. The Essential Guide to Image Processing, 14(1), 715–739. doi:10.1016/B978-0-12-374457-9.00025-1

• [3] Paone, J., & Flynn, P. J. (2011). On the consistency of the biometric menagerie for irises and iris matchers. 2011 IEEE International Workshop on Information Forensics and Security, WIFS 2011. doi:10.1109/WIFS.2011.6123158

• [4] Fenker, S. P., & Bowyer, K. W. (2011). Experimental evidence of a template aging effect in iris biometrics. 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, 232–239. doi:10.1109/WACV.2011.5711508

• [5] “Information technology – Biometric performance testing and reporting - Part 1: Principles and framework.” [Online]. Available: https://www.iso.org/obp/ui/#iso:std:iso-iec:19795:-1:ed-1:v1:en. ;Accessed: 04-Feb-2015].

• [6] K. O’Connor, “Examination of stability in fingerprint recognition across force levels,” p. 89, 2013.

REFERENCES