1. Biometric Measures for Human Identification
D. Adjeroh, B. Cukic, L. Hornak, A. Ross
Lane Department of CSEE
West Virginia University
NC-BSI, December 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. Systems Approach: Port of Entry
Modality, FMR, vulnerability, exceptions, throughput?
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 )
Acceptance,modality, quality?
Local, distributed, or central?
Modality, quality, scalability, update, access ?
False Match Rate, Inconvenience acceptance?
Risk function
False Non Match Rate
NC – BSI 2008
after Cukic et al.

4. Error Rates Test Test Parameter False Reject Rate False Accept Rate
Fingerprint
FVC
[2004]
Exaggerated distortion 2%
FpVTE
[2003]
US govt. operational data
0.1% 1%
Face
FRVT
[2002]
Varied lighting, outdoor/indoor
10%
FRGC
[2006]
Time lapse, varied lighting/expression, outdoor/indoor
Iris
ITIRT [2005]
Indoor environment, multiple visits
0.99%
0.94%
Voice
NIST
Text independent, multi-lingual
5-10%
2-5%
NC – BSI 2008

5. NIST FRVT 2006 Results
NC – BSI 2008

6. Sensor 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: Years 2-5:
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.
NC – BSI 2008