INTEROPERABILITY AND THE

STABILITY SCORE INDEXZach Moore, Stephen Elliott, Kevin O’Connor, Shimon Modi

INTRODUCTION

•Wanted to look at interoperability of fingerprint

images across sensors in the context of

stability

•Quality changes across sensors, but does this

affect stability?

INTRODUCTION

• Shimon, 2008

• Analyzed interoperability of fingerprint sensors

• How this affected system performance

• Minutiae based matching

• O’Connor, 2013

• Looked at the instability of the zoo animals across different force levels

• Created the stability score index (SSI)

RELATED WORK

STABILITY SCORE INDEX

METHODOLOGY

• Cleaned the data

• Only used subjects who had three enrollment captures and three testing captures on all sensors

• Created dataruns

• Ran the data through Megamatcher to get genuine and impostor scores

• Ran the scores through Oxford Wave to get zoo analysis

• Used the zoo analysis to calculate stability scores

METHODOLOGY

•Divided datasets

METHODOLOGY

Sensor Enrollment

Samples

Testing

Samples

Total

Samples

Atmel 483 483 966

Authentec 483 483 966

Crossmatch 483 483 966

Digital Persona 483 483 966

Fujitsu 483 483 966

Futronic 483 483 966

Identix 483 483 966

UPEK S 483 483 966

UPEK T 483 483 966

SAMPLES

• 161 subjects

• 6 captures each

• 3 enrollment

• 3 testing

RESULTS

AVERAGE SSI GROUPING MATRIX

AVERAGE SSI GROUPING MATRIX

SUBJECT 43 STABILITY

SENSOR MATRIX

SENSOR MATRIX SUBJECT 273

SENSOR MATRIX VALUES

SENSOR ENROLL BOXPLOT

SENSOR TEST BOXPLOT

ACTION TYPE MATRIX

ACTION TYPE MATRIX VALUES

ACTION TYPE ENROLL BOXPLOT

ACTION TYPE TEST BOXPLOT

SENSOR TYPE MATRIX

SENSOR TYPE MATRIX VALUES

SENSOR TYPE ENROLL BOXPLOT

SENSOR TYPE TEST BOXPLOT

INTERACTION TYPE MATRIX

INTERACTION TYPE MATRIX VALUES

INTERACTION TYPE ENROLL BOXPLOT

INTERACTION TYPE TEST BOXPLOT

HISTOGRAM OF SSI BY ENROLLMENT

SENSOR

• Data is not normal

• Ran Kruskal-Wallis test

00

1

2

3

4

5

6

41.0- 00.0 41.0 82.0 24.0 65.0 07.0 48.

0.1760 0.1303 1440

0.2782 0.1788 1440

0.1691 0.1361 1440

0.1685 0.1320 1440

0.1896 0.1430 1440

0.1741 0.1410 1440

0.1802 0.1406 1440

0.2023 0.1499 1440

0.1634 0.1313 1440

Mean StDev N

S

ytisn

eD

IS

A

rosneS llornE

T KEPU

S KEPU

xitnedI

cinortuF

ustijuF

anosreP latigiD

hctaMssorC

cetnehtuA

lemt

N lamro

•H0= the median SSI scores are equal

•Ha= the median SSI scores are not equal

KRUSKAL-WALLIS TEST

Sensor H DF P

Atmel 58.80 8 0

Authentec 221.45 8 0

Crossmatch 63.75 8 0

Digital Persona 56.33 8 0

Fujitsu 121.45 8 0

Futronic 81.66 8 0

Identix 102.72 8 0

UPEK S 109.80 8 0

UPEK T 82.62 8 0

KRUSKAL-WALLIS RESULTS

•All p-values resulted in p=0

•Reject H0

• Meaning the medians of the SSIs across the

sensors are significantly different

KRUSKAL-WALLIS RESULTS

CONCLUSION

• Subjects are not stable across different sensors

using SSI

• Enrolling on Authentec produced the worst SSIs

overall, but testing on it did not show the same

pattern

• Predicting how unstable a user will be from

enrollment to testing would increase performance

CONCLUSION

•Look at stability across force levels

•See if type of sensor plays a role (thermal,

swipe, touch, etc.)

•Analyze the image quality of the images and

look for a relationship

FUTURE WORK