This document summarizes research on anti-spoofing algorithms for fingerprint systems. It discusses how fingerprint spoofing works and known spoofing methods. It also reviews approaches for fingerprint liveness detection, including texture-based features, perspiration-based features, and classification results on standard datasets. Finally, it proposes a Bayesian belief network approach to fuse liveness scores with fingerprint matching scores to improve spoof detection performance.

1. Anti-Spoofing Algorithms for
Fingerprint Systems
Emanuela Marasco
12
Lane Department of Computer Science and Electrical Engineering

2. Security of Fingerprint Systems
13
[1] http://nexidbiometrics.com/brazilian-doctor-arrested-for-using-fake-fingerprints/
[2] http://secureidnews.com/news-item/apples-touch-id-spoofed/
• What about Impersonation?
• March 2013, Brazilian doctor accused to use spoof fingerprints to
check-in co-workers not present at work place
• September 2013, the iPhone5S equipped with Touch ID sensor
accepted a spoof fingerprint as live
Police recovered six silicone fingers
After two days only it was released

3. Fingerprint Spoofing
• Liveness detection distinguishes live human biometric presentations
from spoof artifacts [2],[3],[4]
• The liveness of a fingerprint is assessed by a numerical entity
14
[1] T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino. Impact of artificial gummy fingers on fingerprint systems. Optical Security and Counterfeit
Deterrence Techniques IV, 4677:275–289, January 2002.
[2] S. Schuckers. Spoofing and anti-spoofing measures. Information Security Technical Report, 7(4):56–62, 2002.
Making artificial fingerprints directly from a live finger [1]
• Fingerprint systems are vulnerable to spoof presentations [1]
[3] D. Yambay, L. Ghiani, P. Denti, G. Marcialis, F. Roli and S. Schuckers. LivDet 2011 – Fingerprint Liveness Detection Competition 2011. The 5th
International Conference on Biometrics (ICB), pages 208-215, March 2012.
[4] G. Marcialis, A. Lewicke, B. Tan, P. Coli, D. Grimberg, A. Congiu, A. Tidu, F. Roli, and S. Schuckers. First international fingerprint liveness detection
competition (LivDet09). The 15th International Conference on Image Analysis and Processing (ICIAP), pages 12–23, September 2009.

4. The considered dangerous scenarios
15
http://www2.washjeff.edu/users/ahollandminkley/Biometric/index.htmlThe images are taken from
• A person enrolls using a live finger
• A spoof artifact of the true finger is used during verification [1]
live spoof
Enrollment Verification
[1] S. Schuckers. Spoofing and anti-spoofing measures. Information Security Technical Report, 7(4):56–62, 2002.

5. Live or Spoof?
16
Images taken from LivDet09

6. Vulnerability Degree vs. Fingerprint Sensing
17
• Optical sensors: light entering the prism is reflected at valleys
and absorbed at the ridges
• Physiological vulnerability: materials with light reflectivity similar
to that of the skin
• Device: differences in ergonomics, arrangement of elements
• Capacitive sensors: the finger is modeled as the upper electrode
of a capacitor
• Fingerprint in gelatin are more dangerous
Spoof fingerprints obtained using the
same material (silicone) but scanned
by two different optical devices
(CrossMatch and Biometrika from
LivDet09

7. How to make Fingerprint Spoof?
18
• Materials which can be scanned (e.g., play-doh, gelatin, …)
[1] S. Schuckers. Spoofing and anti-spoofing measures. Information Security Technical Report, 7(4):56–62, 2002.

8. Known Spoofing Methods
19

9. Liveness Detection Approaches
20

10. Texture-based Features
21
Original Image De-noised Image Residual Noise
Original Image De-noised Image Residual Noise
L
I
V
E
S
P
O
O
F
• Standard deviation of the residual noise
• Noise components are due to the coarseness of the fake
finger surface

11. Texture-based Features
22
• First order statistics
Uniformity of grey
levels is less in
live

12. Perspiration-based Features
23
• Individual pore spacing
1. Image transformed into the ridge signal
2. Analysis in the Fourier domain
3. Total energy associated to the frequencies
corresponding to the spatial frequencies of pores
High
resolution
sensor
1000 dpi
• Intensity-based
• Spoofs are distributed in the dark
• Grey Level (GL) Ratio:
# of pixels with GL in (150, 253)/(1,149)

13. Data details
24
DATA SET LivDet13
Dataset Scanner
Live Training
Samples
Live Training
Fingers
Live Testing
Samples
Live Testing
Fingers
1 Biometrika 1000 200 1000 200
2 Italdata 1000 200 1000 200
DATA SET LivDet13
Dataset Scanner
Spoof Training
Samples
Spoof Training
Fingers
Spoof Testing
Samples
Spoof Testing
Fingers
1 Biometrika 1000 100 1000 100
2 Italdata 1000 100 1000 100

14. Classification Results
25
• Texture-based
• First order statistics
• Second order statistics
• Power spectrum analysis
• Local Binary Pattern
• Weber Local Descriptor
• Perspiration-based
• Intensity-based
• Quality-based
• Pores-based
Marasco and Sansone 2012 [1]
• FLR=12.6%; FSA=12.3%
LivDet09 databases
• FLR=11.61; FSA=6.2%
LivDet11 Digital Persona
• FLR=11.61; FSA=6.2%
LivDet13
Marasco and Sansone [1] combined
with Gragnaniello et al. [2]
• FLR=11.3; FSA=1.8%
LivDet13 Biometrika
• FLR=4.9; FSA=2.1%
LivDet13 Italdata
[1] E. Marasco and C. Sansone. Combining perspiration- and morphology-based static features for fingerprint liveness detection.
Pattern Recognition Letters, 33:1148–1156, 2012
[2] C. Sansone D. Gragnaniello, G. Poggi and L. Verdoliva. Fingerprint liveness detection based on weber local image descriptor.
IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BioMs), pages 1–5, 2013

15. BBN-based to fuse liveness with
match scores
26
BBN: Bayesian Belief Network
• The liveness measure is assumed to influence match scores
• The joint probability is based on the topology of the network:
• IIII identity state
• SSSSgggg liveness state of gallery
• SSSSpppp liveness state of probe
Events:
P(I)P(Sg)P(Sp)Sg)|P(lgSp)|P(lpI)lp,lg,|P(m
Sg)Sp,I,lg,lp,P(m, =

16. Bayesian Inference
27
From previous slide
The probability of identity state is based
on both match scores and liveness values
Target: given liveness and match scores, what is the probability
that both probe and gallery samples pertain to the same identity
and both are live samples?
lg)lp,P(m,
Sg)Sp,I,lg,lp,P(m,
lg)lp,m,|SgSp,P(I, =
lg)lg)P(lp,lp,|P(m
Sg)P(I)Sg)P(Sp)P(|Sp)P(lg|I)P(lplp,lg,|P(m
=
lp)lg,|P(m
I)lp,lg,|P(I)P(m
P(lg)
Sg)P(Sg)|P(lg
P(lp)
Sp)P(Sp)|P(lp
=
lp)lg,|P(m
lp)lg,|lg)P(mlp,m,|P(I
lg)|Sg(P)lp|Sp(P=
lp)lg,m,|P(Ilg)|Sg(P)lp|Sp(P=
Independence between
andlg lp
)lplg,|I(P)I(P =

17. 28
Results (Silicone, training rate 50%)
Method Verification
FMR% FNMR%
Spoof Detection
FSAR% FLRR%
Global
GFAR% GFRR%
4 (FB) 0.103 5.044 5.244 12.516 0.308 14.217
4 (NN) 0.002 5.187 5.297 12.558 0.241 15.696
• Method 1 achieves best spoof detection performance with a less
accurate liveness detector
Method Verification
FMR% FNMR%
Spoof Detection
FSAR% FLRR%
Global
GFAR% GFRR%
4 (FB) 0.089 15.866 0.316 12.434 0.046 4.195
4 (NN) 0.011 16.415 0.320 12.475 0.008 4.634
Results (Gelatin, training rate 50%)
• The lowest global error rates are achieved with gelatin, where
the liveness measure is accurate

18. Open Issues
29
• Testing in the presence of unknown spoof materials
• Learning-based
• Certification
• Integration with the matcher
• Performance metrics

19. Questions?
30