BIOMETRICS IN ABC: COUNTER-SPOOFING RESEARCH Hong Wei, Lulu Chen and James Ferryman Computational...

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BIOMETRICS IN ABC: COUNTER-SPOOFING RESEARCH

Hong Wei, Lulu Chen and James Ferryman

Computational Vision Group School of Systems Engineering

University of Reading, UK

11th October 2013

FRONTEX - 2nd Global Conference and Exhibition on Future Developments of Automated Border Control (ABC) 10/11 October 2013, Warsaw, Poland

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Outline of the presentation• Introduction• Research on counter spoofing attacks

– for face– for fingerprint– for iris

• Challenges and future research


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Introduction• ABC requires: fast, secure identity verification.• EU FastPass project (www.fastpass-project.eu):

harmonised, modular reference system for future European ABC.

• Biometrics: face, fingerprint and iris – important for ABC.• Potential vulnerability: sensor-level attacks – “spoofing”.• Counter-spoofing measure: crucial for FastPass.


Heavy traffic and pressure at current border control – how to ease these?

FastPass

http://www.fastpass-project.eu/

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Face


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Face counter spoofing approaches • Three categories in developing face anti-

spoofing algorithms− Motion analysis: make use of significant

difference between motions of planar objects and real face (3D) in optical flow fields.

− Texture analysis: extract image texture features which reflects difference between real face and printed or replayed faces.

− Liveness detection: detection of life signs such as eye blinking, lip movement, etc.


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Relevant competitions • Competitions on counter measures to 2D facial spoofing

attacks• First with IJCB 2011: 6 teams [Chakka, et al 2011] .• Second with ICB 2013: 8 teams [Chingovska, et al

2013]• Participants: academic teams• Replay-Attack face spoofing database:

• Printed photographs• Photographs on a mobile device (iPhone / iPad)• Videos replayed on a mobile device


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Summary of the 2013 competition


Team 1 2 3 4 5 6 7 8

Texture based

Yes No Yes Yes Yes Yes Yes Yes

Motion based

Yes No Yes Yes No No No No

Liveness based

No Yes No No No No No No

Fusion Feature Score Score Feature Feature Score No No

Methods used in algorithms development

• Database: Replay-Attack Face Spoofing Database• Teams 1&4 achieved 100% accuracy in both development and test.• All 8 teams developed highly sophisticated methods, and some

introduced methods beyond the three categories, such as human pulse.

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Open issues and challenges• Facial features:

• Change over time• Similarity between family members:

e.g. twins, father and son.• Capture:

• Moving subject• Efficient sensor-level fusion

(VR+NIR, stereo vision)• 2D face only: 3D attacks should be

added, e.g. 3D face masks.


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Fingerprint


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Fingerprint sensing technologies Optical sensors

Relatively big in size In border control, biometric enrolment

Solid state fingerprint sensors Different types, most common: capacitive Can be compact Sensitive to electrostatic discharges

Multiple spectral imaging sensor 3D scanner

Can be touchless


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Fingerprint counter-spoofing methods Solutions: Hardware and Software based Hardware:

Pulse oximetry, smell, temperature, blood pressure, heart beat etc.

Requires additional hardware Software:

Perspiration pattern Skin distortion Pores Image quality measure


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Software based


Perspiration patterns

Skin distortion

Real

Fake

Pore distribution

Image quality measure

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Fingerprint counter-spoofing competitions LivDet: Fingerprint Liveness Detection Competition

Since 2009, every two years Software based methods Multiple sensors:

• Optical and swipe Various reproduction materials:

• Gelatin• Silicon• Play-doh• Latex


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Fingerprint LivDet


Participants Submissions Best results (avg. across datasets)

LivDet 2009 4 4 Dermalog: Ferrfake: 5.4%Anonymous 2: Ferrlive: 20.1%

LivDet 2011 3 Part I: algorithms 3 Federico: 26.60% FerrfakeDermalog: 17.60% Ferrlive

2 Part II: system 2 Dermalog: 0.8% FerrfakeGreenbi: 38.8% Ferrlive

LivDet 2013 9 Part I: algorithms 11 Dermalog: 1.10% FerrfakeFederico: 11.96% Ferrlive

Part II: system 2 Morpho: 0.0% FerrfakeMorpho: 1.4% Ferrlive

• Ferrfake: a false acceptance of a spoof image. • Ferrlive: a false rejection of a live subject.

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Open issues and challenges• Various spoofing materials

• Recent approaches:• Handles certain types well • Poor performance on other materials

• New methods should:• Detect and handle all types of spoofing materials• Combine successful algorithms• Enable more balanced ferrfake and ferrlive rates

• Touchless fingerprint scan


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Iris


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Iris counter-spoofing approaches• Spectrographics based: optical properties

• Purkinje reflection• Retina light reflection: ‘Red eye’ effect

• Image quality measure• Behaviour based: dynamic properties

• Eye hippus• Pupil and iris constriction and dilation• Eyelid blinking

• Other:• 3D structure


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Iris counter spoofing competitions• LivDet-Iris: First Liveness Detection-Iris

Competition 2013

• Held by IEEE BTAS Conference (September 29 – October 2, 2013)


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Open issues and challenges• FastPass:

• Data capturing: involves additional noise• Small target• Long-distance• On-the move• Illumination, focus

• Research: • Data collection: lack of data for training• Lack of standardisation on spoofing experimental

datasets


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Challenges• Current biometric systems:

– Have counter-spoofing mechanism integrated.– However, research show vulnerability from spoofing

attacks (e.g. TABULA RASA Spoofing Challenge 2013).• Arms race: between spoofing and counter-spoofing.

– Spoofing using new technologies, e.g. Hand-held mobile devices.

• Increased throughput and intuitive user-friendly devices.• Lack of training data.• Data interchange format.• E-Passport storage capacity.


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Future of counter spoofing• Multimodal:

– Increase difficulty to spoof multiple traits.– Not every person has all biometric features.

• Multi-sensor system: – Visible range and Near IR– 2D and 3D

• Combined approaches: – Handle different types of materials.

• Tackle continuously updated and practised spoofing attacks.

• Increase difficulty for replication process.


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Future research• Explore and develop new algorithms on counter

spoofing measures and attacks in ABC applications. • Assemble ABC-specific databases with more realistic

spoofing attacks.• Create a framework for counter spoofing measures.• Analyse data fusion effects at a quantitative level:

– At the feature level for a single trait;– At the decision (score) level for multiple traits;– Combination of feature fusion and trait fusion;– Adaptive fusion schemes taking additional

information (quality, thresholds) into account.


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Summary

• ABC demands harmonized capturing devices adopting international standards– non intrusive and enable remote biometric capture– faster, more secure, efficient, seamless– robust counter spoofing detection

Subject to appropriate legal and ethical controls


Thank you.&

Questions


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www.fastpass-project.eu

http://www.fastpass-project.eu/

BIOMETRICS IN ABC: COUNTER-SPOOFING RESEARCH Hong Wei, Lulu Chen and James Ferryman Computational...

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