Lessons Learned from Implementing Large-Scale Biometrics Identity Programs

Transcription

1 Lessons Learned from Implementing Large-Scale Biometrics Identity Programs CTST 2008 May 13, 2008 Copyright 2008 International Biometric Group

2 International Biometric Group Leading biometric research and testing, consulting and integration firm since 1996 Over 250 biometrics engagements conducted Combine technical and business expertise Technology-neutral, vendor-independent experts 90% employees have secret/top secret clearances Lower Manhattan Headquarters with Demonstration Facility Geographic Diversity: Washington, DC; London; San Francisco Copyright 2008 International Biometric Group Page 2

3 Representative IBG Clients Technology & Transportation Government Agencies Financial Services American Airlines AAMVA AIG Diebold California DMV Charles Schwab EDS White House OSTP Chase Manhattan Bank Ingersoll-Rand Department of Homeland Security Citibank Intel Ontario, Canada MBS Dresdner Bank JFK Airport National Institute of Justice Fidelity Investments Lockheed Martin NYPD FSTC Microsoft Transport Canada Visa Raytheon U.S. Navy/SPAWAR World Bank Honeywell EU Commission Capital One Marconi Comm. European Biometric Forum MasterCard Siemens HKSAR Booz Allen Hamilton Copyright 2008 International Biometric Group Page 3

4 World of Biometrics Copyright 2008 International Biometric Group Page 4

5 Lessons from Large-Scale Integration Projects Copyright 2008 International Biometric Group Page 5

6 Lesson 1 Security Planning Involve security early in project planning stages Develop relationships with key decision makers Provide enough time for security gates and approvals Discover requirements early auditing, data retention, access control, network characteristics Copyright 2008 International Biometric Group Page 6

7 Lesson 2 Performance Requirements Push end customer for processing and response time requirements need to be as accurate as possible Dramatically affects sizing as system capacity increases Agree on definitions and importance of performance result metrics FMR, FNMR, FTE, FTA, etc. Initial deployment, bulk ingestion, duplicate checking, steady state processing, peak and average loads Copyright 2008 International Biometric Group Page 7

8 Lesson 3 Cost Estimation Hardware: acquisition devices (if needed), matching servers, redundant storage, database replication, disaster recovery, anticipated scalability Software: front end and administrative tools, custom auditing/reporting functionality, queuing systems, transaction managers, licensing Central and distributed system maintenance Performance requirements (e.g. response time) Testing and piloting requirements Training: initial and ongoing Copyright 2008 International Biometric Group Page 8

9 Lesson 4 End User Interaction How much information do End Users have access to? want? need? understand? Handling of error cases troubleshooting capability, expert analysis availability Are users or analysts able or required to collaborate? In the end what matters is actionable results for decisions Copyright 2008 International Biometric Group Page 9

10 Lesson 5 Black Box Matchers Matchers are optimized to work as designed violating that will only decrease performance Concentrate on input/output optimize other parts of the system to work with matchers How much development/integration is required? Work with vendors to understand all configurable parameter options, especially enrollment quality and matching thresholds Develop strategy to throttle and load balance across multiple modalities and algorithms where applicable Copyright 2008 International Biometric Group Page 10

11 Lesson 6 Virtualization Some matchers react badly to virtualization, making a single physical resource function as multiple virtual resources Inquire with vendor during initial research and information sessions Option 1: Work within matchers constraints Option 2: Test out creative ideas with pilots or research projects in early stages Some customers require virtualized servers in data centers Copyright 2008 International Biometric Group Page 11

12 Lesson 7 Binning and Filtering Binning: pre-sorting a biometric database based on categories of biometric characteristics Filtering: classifying biometric data based on metadata (i.e. gender, age, height, weight) Improves performance if implemented correctly Requires customized, intelligent central processing engine COTS middleware often difficult to use in conjunction with these rules Complicates match threshold management need different thresholds for different types of targeted search requests Copyright 2008 International Biometric Group Page 12

13 Lesson 8 Quality Assessment and Enhancement Requires detailed logical workflows Coordination with matcher processes Quality thresholds require study to identify and tune over time for system optimization Increases storage requirement to accommodate processed images Only highly trained users should have access to manual QA or enhancement tools depending on the modality Copyright 2008 International Biometric Group Page 13

14 Questions Thank You Peter VanBuskirk International Biometric Group One Battery Park Plaza, 29 th Floor New York, NY Copyright 2008 International Biometric Group Page 14