biometric measures for human identification d. adjeroh, b. cukic, l. hornak, a. ross lane department...
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Biometric Measures for Human Identification
D. Adjeroh, B. Cukic, L. Hornak, A. Ross
Lane Department of CSEEWest Virginia University
NC-BSI, December 2008
NC – BSI 2008 2
Problem Statement
• Current biometric systems at the US borders rely on fingerprint and face recognition (US-VISIT). We will analyze the use of other biometric modalities (iris, palm, face, voice) and their combinations for border security.
• Methodology– Lab and field experiments to study the maturity,
reliability, cost, performance, and feasibility of new biometric modalities in the context of border security.
NC – BSI 2008 3
Risk function
Systems Approach: Port of Entry
Traveler Queues
Watch Lists / Identity DB
Legend=Required Signal=Optional Signal= Movement
Public Key Directory
Secondary Inspection / Detainment
Border Access
=Optional Movement
Inspection Stations(w/ biometric )
after Cukic et al.after Cukic et al.
Local, distributed, or central?
Modality, quality, scalability, update, access ?
Acceptance,modality, quality?
Modality, FMR, vulnerability, exceptions, throughput?
False Match Rate, Inconvenience acceptance?
False Non Match Rate
NC – BSI 2008 4
Error RatesError Rates
Test Test ParameterFalse Reject
Rate False Accept
Rate
Fingerprint
FVC[2004]
Exaggerated distortion 2% 2%
FpVTE[2003]
US govt. operational data
0.1% 1%
Face
FRVT[2002]
Varied lighting, outdoor/indoor
10% 1%
FRGC[2006]
Time lapse, varied lighting/expression,
outdoor/indoor10% 0.1%
IrisITIRT
[2005] Indoor environment,
multiple visits0.99% 0.94%
VoiceNIST
[2004]Text independent,
multi-lingual5-10% 2-5%
NC – BSI 2008 5
NIST FRVT 2006 Results
NC – BSI 2008 6
Sensor InteroperabilitySensor Interoperability
A. Ross and R. Nadgir, "A Calibration Model for Fingerprint Sensor Interoperability", Proc. of SPIE Conference on Biometric Technology for Human Identification III, (Orlando, USA), April 2006.
NC – BSI 2008 7
Multimodal Biometric Systems
• Multiple sources of biometric information are integrated to enhance matching performance
• Increases population coverage by reducing failure to enroll rate
• Anti-spoofing; difficult to spoof multiple traits simultaneously
Fingerprint Face Hand geometry Iris
NC – BSI 2008 8
Deployment Problems
• Sensor Interoperability• Missing information from some modalities• Non-ideal capture
– Non-cooperative subjects or capture problems– surveillance scenarios, i.e., identifying risk early
• Varying risk tolerance• Maximizing identification rates while minimizing
inconvenience and disruption of border crossing flow.
NC – BSI 2008 9
Leverage
• The Center for Identification Technology Research (NSF I/UCRC)
• Biometrics: Performance, Security and Social Impact, (NSF and DHS – Human Factors)• Performance analysis, multimodal biometric database
collections, familiarity with port of entry applications. • WVU is academic partner with the FBI Center of
Excellence in Biometrics• Large scale data collection for the New Generation Identification
project.
NC – BSI 2008 10
Deliverables
• Year 1:– Analysis of existing performance studies, – Defining specific border application scenarios and their
requirements, – Definition of lab experiments – Definition of field experiments.
• Years 2-5:– Experimental results – Modality recommendations – Multi-modal fusion, – System aspects and recommendations.
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