Biometric Measures for Human Identification

D. Adjeroh, B. Cukic, L. Hornak, A. Ross

Lane Department of CSEEWest Virginia University

NC-BSI, December 2008

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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.

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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

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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%

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NIST FRVT 2006 Results

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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.

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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

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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.

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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.

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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.