1 / 10

Biometric Measures for Human Identification

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.

kairos
Download Presentation

Biometric Measures for Human Identification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. Traveler Queues Inspection Stations (w/ biometric ) Public Key Directory Secondary Inspection / Detainment Watch Lists / Identity DB Border Access Legend =Required Signal =Optional Signal = Movement =Optional Movement Systems Approach: Port of Entry Modality, FMR, vulnerability, exceptions, throughput? Acceptance,modality, quality? Local, distributed, or central? Modality, quality, scalability, update, access ? False Match Rate, Inconvenience acceptance? Risk function False Non Match Rate after Cukic et al. NC – BSI 2008

  4. Error Rates 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. Fingerprint Face Hand geometry Iris 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 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. NC – BSI 2008

More Related