Biometric Measures for Human Identification D. Adjeroh, B. Cukic, L. Hornak, A. Ross Lane Department of CSEE West Virginia University NC-BSI, December 2008

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

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.

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

NIST FRVT 2006 Results NC – BSI 2008

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

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

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

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

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