The increasing use of distributed authentication architecture has highlighted interoperability issues of biometric systems. This presentation highlights the ongoing efforts at understanding fingerprint sensor interoperability. BSPA Labs has conducted experiments over the past few years aimed at addressing the challenges related to sensor interoperability. This presentation was given at a research seminar which covered the following: Importance of fingerprint sensor interoperability, sources of issues related to sensor interoperability, analysis framework for evaluating sensor interoperability, discussion of experimental results and its practical applicability

1. Online Research Seminar:
Fingerprint Sensor Interoperability Performance
Shimon K. Modi, Ph.D.
Shimon K Modi Ph D
Director of Research, BSPA Labs
Biometric Standards, Performance, and Assurance Laboratory | www.bspalabs.org
Purdue University, Department of Industrial Technology

2. Agenda
 Problem statement & Goal
 Data collection protocol
 Results
C l i
Conclusions and Future work
d k
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

3. Problem Definition
Thermal Swipe Optical Touch Capacitive Touch
 Matching fingerprints collected on different types
of sensors increases probability of false matches
of sensors increases probability of false matches
and false non‐matches
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

4. Goal
Acquisition
Technology
Interaction Sensor
Type Characteristics
Sensor
Interoperability
 Create a statistical analysis framework to examine the
effect of sensor dependent distortions and variations
on False Non Match Rate (FNMR) of mixed datasets
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

5. Sensor Description
 8 commercially available fingerprint sensors
8 commercially available fingerprint sensors
 1 thermal swipe sensor
 1 capacitive swipe sensor
4O i l
4 Optical touch sensors
h
 2 Capacitive touch sensors
 All sensors were 500 dpi resolution
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

6. Data Collection Protocol
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

7. Description of Participants
Total 190
Gender Male Female
131 59
Occupation Manual Laborer Office Worker
17 173
Age Groups < 30 years 30‐50 years > 50 years
156 23 11
Handedness Right Left Ambidextrous
164 23 3
Ethnicity Caucasian Black Hispanic American
American Asian Other
Indian
133 8 11 1 34 3
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

8. Data Analysis Methodology
• VeriFinger extractor
• Descriptive Stats
Minutiae
Count • Test of similarity
• NFIQ
• Descriptive Stats
Descriptive Stats
Image
Quality • Test of Similarity
• VeriFinger matcher
• Native and mixed datasets
FNMR
FNMR
Matrix • Test of proportions
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

9. VeriFinger 5.0 Extractor Minutiae Count Analysis
Dataset n M SD
S1 1140 41.72 10.57
S2 1122 28.32 10.73
S3 1140 40.25 10.12
S4 1128 30.74 8.06
S5 1110 24.38 6.87
S6 1134 38.62 9.18
S7 1134 27.53 7.69
S8 1122 26.15 6.73
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

10. Minutiae Count Analysis
 Minutiae count extracted using VeriFinger
g g
Overall Hypothesis:
H10: µi minutiae_count = µ2 minutiae_count……..= µ8 minutiae_count
H1A: µi minutiae_count ≠ µ2 minutiae_count……..≠ µ8 minutiae_count
: µ ≠ µ ≠ µ
Pairwise Comparisons:
H1’0: µi minutiae_count = µj minutiae_count for all i ≠ j
H1’A: µi minutiae_count ≠ µj minutiae_count for all i ≠ j
 Rejected null hypothesis H10
 All pairs statistically significant at α = 0.05
p y g
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

11. NFIQ Image Quality Analysis
Dataset N M Median
S1 1140 1.29 1
S2 1122 1.91 2
S3 1140 1.75 1
S4 1128 2.00 2
S5 1110 2.18 2
S6 1134 1.77 2
S7 1134 2.03 2
S8 1122 1.58 2
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

12. Image Quality Analysis
 Image quality computed using NFIQ
g q y p g Q
Overall Hypothesis:
H20: µi qscore = µ2 qscore……= µ9 qscore
: µ = µ =µ
H2 A: µi qscore ≠ µ2 qscore…..≠ µ9 qscore
Pairwise Comparisons:
Pairwise Comparisons:
H2’0: µi qscore = µj qscore for all i ≠ j
H2’A: µi qscore ≠ µj qscore for all i ≠ j
 NFIQ uses a 3‐layer feed forward nonlinear perceptron model
to predict the image quality values based on the input feature
vector of the fingerprint image
vector of the fingerprint image
 Performed a Kruskal Wallis Test of similarity of ranks
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

13. Tukey’s HSD Pairwise Test
D2 D3 D4 D5 D6 D7 D8
D1 S S S S S S S
D2 S S S S S S
D3 S S S S S
D4 S S NS S
D5 S S S
D6 S S
D7 S
 S‐ Significant difference
S Significant difference
 NS – Not Significant
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

14. False Non Match Rate (FNMR) Matrix Analysis
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

15. FNMR Matrix – Fixed FMR 0.1%
TEST
D1 D2 D3 D4 D5 D6 D7 D8
D1 0.47 6.79 5.60 4.44 5.61 31.03 11.30 3.76
D2 7.33 5.05 6.49 7.44 10.92 16.18 10.83 6.11
E
N D3 8.57 4.78 0.24 1.07 1.28 2.73 1.79 0.54
R
O D4 5.10 4.44 0.95 0 1.39 1.60 1.83 1.08
L
D5
5 5.56 7.64
.6 0.85 0.85 0. 8
0.78 4.42
. 2.23
. 3 0.
0.42
D6 33.86 13.42 2.99 1.30 4.47 0.17 1.41 2.15
D7 14.92 9.89 1.73 2.12 2.35 2.71 0.94 1.85
D8 2.87 2.96 0.12 0.90 0.49 1.73 0.77 0.11
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

16. Test of Homogeneity of Proportions
 FNMR Matrix is useful for numerical comparison
p
 Test of Homogeneity of FNMR was performed
between native FNMR and mixed FNMR
b i d i d
 Compare multiple proportions using Marascuillo
Compare multiple proportions using Marascuillo
Procedure
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

17. Results
D1 D2 D3 D4 D5 D6 D7 D8
D1 S S S S S S S
D2 NS NS NS S S S NS
D3 S S NS NS S S NS
D4 S S NS NS S S NS
D5 S S NS NS S NS NS
D6 S S S NS S NS NS
D7 S S NS NS NS NS NS
D8 S S NS NS NS S NS
 S‐ Significant difference
S Significant difference
 NS – Not Significant
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

18. Impact of Quality on Interoperable FNMR
 Removal of low quality images will reduce FNMR
q y g
of native and interoperable datasets
 But will it make the FNMR of mixed datasets
similar to FNMR of native datasets?
i il f i d ?
 FNMR matrix regenerated using VeriFinger 5 0 for
FNMR matrix regenerated using VeriFinger 5.0 for
datasets comprised of images with NFIQ score of
1, 2, or 3
 The test of homogeneity of proportions was rerun
on native and mixed dataset FNMR
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

19. Results
D1 D2 D3 D4 D5 D6 D7 D8
D1 S S S S S S S
D2 NS NS NS S S NS NS
D3 S S NS NS S S NS
D4 S S NS NS S S NS
D5 S S NS NS S NS NS
D6 S S S S S S S
D7 S S NS NS NS NS NS
D8 S S NS NS NS S NS
 S Significant difference
S‐ Significant difference
 NS – Not Significant
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

20. Consistency of Placement
 Interaction with different sensors can lead to inconsistent placement
due to sensor design, interaction type, capture area
 Metric of consistency – detection of core
Core detected in both images Core detected in one image
Samples from different sensors – Samples from different sensors –
Correctly Verified Incorrectly rejected
Core detected in none of the images
Samples from same sensor – Correctly verified
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

21. Consistency of Placement
 Interaction with different sensors can lead to
inconsistent placement due to sensor design,
interaction type, capture area
 Metric of consistency detection of core
Metric of consistency – detection of core
D1,D6
D6,D1
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

22. Interoperability ‐ Interaction & Technology
A l d i t
Analyzed interoperability of datasets separated by
bilit f d t t t db
interaction and technology type
 Created three datasets
1. Fingerprints collected from swipe sensors
g p p
2. Fingerprints collected from optical touch sensors
3. Fingerprints collected from capacitive touch sensors
g p p
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

23. Interoperability ‐ Interaction & Technology
TEST
Swipe
p Optical Capacitive
p p
Touch Touch
E Swipe 4.81 11.75 6.58
N
R
O Optical 11.93 1.53 1.89
L Touch
Capacitive 4.76 1.47 0.45
Touch
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

24. Conclusions
 No technology specific impact on similarity of
gy p p y
minutiae count and quality score
 Image quality analysis reduced FNMR, but did not
increase similarity of FNMR
i i il i f
 Consistency of placement improved performance
of mixed datasets
of mixed datasets
 Data collected from same interaction type had
lower FNMR
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

25. Future Work
 Apply the analysis framework to test False Match
pp y y
Rates (FMR)
 Removing low quality images on interoperable
datasets did not make FNMR more similar
d did k i il
 Analyze which factors are affected by removal of low quality images
 Further analysis into skin characteristics and its
Further analysis into skin characteristics and its
impact on image quality
 Develop a detailed ridge spacing profile which can
be used as a sensor agnostic method for
transforming images and compensate for
 El ti D f
Elastic Deformation
ti
 Capture mechanisms
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

26. Upcoming Research Seminar
 Paper presented at BTAS 2009-
2009
http://www.bspalabs.org/files/185/BTAS%20Fprint%20Se
nsor%20Interop.pdf
 Slides available at
http://www.bspalabs.org/presentations-on-
demand
 Next seminar will be on Human Biometric
Sensor Interaction – Dr Eric Kukula
Dr.
 Feb 10th at 3:30pm (EST)
 Sign up at
http://www.bspalabs.org/archives/1236
Biometric Standards, Performance & Assurance Laboratory | www.bspalabs.org

27. Thank You!
Questions…
Contact Information:
Shimon K. Modi, Ph.D.
modis@purdue.edu
BSPA L b
Laboratory | www.bspalabs.org
b l b
Purdue University, Knoy Hall of Technology
401 North Grant Street
West Lafayette, IN 47907 2021
47907-2021
Phone: (765) 494-2311
Fax: (765) 496-2700