Anti-Spoofing Algorithms for

Fingerprint Systems

Emanuela Marasco

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Lane Department of Computer Science and Electrical Engineering

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

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

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[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.

The considered dangerous scenarios

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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.

Live or Spoof?

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Images taken from LivDet09

Vulnerability Degree vs. Fingerprint Sensing

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• 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

How to make Fingerprint Spoof?

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• 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.

Known Spoofing Methods

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Liveness Detection Approaches

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Texture-based Features

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Original Image De-noised Image Residual Noise

Original Image De-noised Image Residual Noise

LIVE

SPOOF

• Standard deviation of the residual noise

• Noise components are due to the coarseness of the fake finger surface

Texture-based Features

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• First order statistics

Uniformity of grey levels is less in

live

Perspiration-based Features

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• 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)

Data details

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DATA SET LivDet13

Dataset ScannerLive 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 ScannerSpoof Training

Samples

Spoof Training

Fingers

Spoof Testing

Samples

Spoof Testing

Fingers

1 Biometrika 1000 100 1000 100

2 Italdata 1000 100 1000 100

Classification Results

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• 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

BBN-based to fuse liveness with

match scores

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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(lg Sp)|P(lp I)lp,lg,|P(m

Sg)Sp,I,lg,lp,P(m, =

Bayesian Inference

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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(Ilg)|Sg(P)lp|Sp(P =

lp)lg,m,|P(I lg)|Sg(P )lp|Sp(P =

Independence between

and lg lp

)lplg,|I(P)I(P =

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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

Open Issues

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• Testing in the presence of unknown spoof materials

• Learning-based

• Certification

• Integration with the matcher

• Performance metrics

Questions?

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