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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. Outline of the presentation. Introduction Research on counter spoofing attacks for face
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BIOMETRICS IN ABC: COUNTER-SPOOFING RESEARCH Hong Wei, Lulu Chenand James Ferryman Computational Vision Group School of Systems EngineeringUniversity of Reading, UK11th October 2013
Outline of the presentation • Introduction • Research on counter spoofing attacks • for face • for fingerprint • for iris • Challenges and future research
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. FastPass Heavy traffic and pressure at current border control – how to ease these?
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.
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
Summary of the 2013 competition 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.
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.
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
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
Software based Pore distribution Perspiration patterns Skin distortion Image quality measure Real Fake
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
Fingerprint LivDet • Ferrfake: a false acceptance of a spoof image. • Ferrlive: a false rejection of a live subject.
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
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
Iris counter spoofing competitions • LivDet-Iris: First Liveness Detection-Iris Competition 2013 • Held by IEEE BTAS Conference (September 29 – October 2, 2013)
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
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.
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.
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.
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 www.fastpass-project.eu