Biometrics Workshop. The evolution of large-scale biometric architecture. Facilitators. Mark Crego, Accenture Mike Matyas, Mount Airey Group

Transcription

1 2020 Workshop The evolution of largescale biometric architecture Facilitators Mark Crego, Accenture Mike Matyas, Mount Airey Group

2 Approach and Agenda Workshop Goal: An open discussion on the future of largescale biometric architecture Agenda Brief Context Setting Where we ve come from The danger of bad vision Needs View 2020 Vision Roundtable Discussion Client of the future Network Service Oriented Architecture Data Matching Intelligence 2

3 Fingerprint Matching Circa

4 FBI Identification Division Operations 4

5 A view from five years ago Future Business and Volume Demands Drive Future Architecture 2,000 1,500 1, Transactions per day (K) ,300 2,000 People in DB (M) Facts (so we thought ) Transactions per day will grow to 2 million by 2009 People stored will grow to 450 million by 2009 Number of additional s will triple by 2007 Implications Need to increase transaction throughput by over 10 times Need to Increase data storage capacity by 40 times Need to improve ability to support new s 5

6 A NeedsBased View Needs in 2020 and beyond Expanded Modalities (finger, face, iris, palm, DNA ) Realtime all the time Available everywhere Access to all the data Criminal Justice data Immigration Civilian Department of Defense Privacy and Security Architecture Requirements Modular, Service Oriented Architecture Extreme scalability Ubiquitous Access Interoperability via Shared Services Privacy and Security 6

7 2020 Vision: Realtime Identification Anytime, Anywhere Whoever needs the data gets it securely, privately. Standardsbased services Identification Matching Enrollment Notification Support any modality Identity emerges from the data Zero: Downtime Failures to enroll False accepts False rejects 7

8 2020 Vision Reference Architecture Internet Enterprise Service Bus (Secure, Standardsbased) Universal Translator) Workflow & Transaction Management Intelligence Local Service Bus (standardsbased abstraction) Modular Matching Tier 8

9 2020 Vision Discussion Topic The Client of the Future Enterprise Service Bus (Secure, Standardsbased) Universal Translator) Workflow & Transaction Management Internet Intelligence Local databases Antispoofing Provisioning Security Authentication Authorization Sensors Modularity Local Service Bus (standardsbased abstraction) Modular Matching Tier 9

10 2020 Vision Discussion Topic The Network Enterprise Service Bus (Secure, Standardsbased) Universal Translator) Workflow & Transaction Management Local Service Bus (standardsbased abstraction) Modular Matching Tier Internet Intelligence Internet or private network? Security Model Ubiquitous access Satcom Bandwidth PKI Local or Central caching of data Grid/offgrid 10

11 2020 Vision Discussion Topic Service Oriented Architecture Enterprise Service Bus Workflow Middleware v buildyourown Local Service Bus Enterprise Service Bus (Secure, Standardsbased) Universal Translator) Workflow & Transaction Management Internet Intelligence Local Service Bus (standardsbased abstraction) Modular Matching Tier 11

12 2020 Vision Discussion Topic Data Emergence Enterprise Service Bus (Secure, Standardsbased) Universal Translator) Workflow & Transaction Management Local Service Bus (standardsbased abstraction) Modular Matching Tier Internet Intelligence Identity Management versus Identity Emergence Semantic Representations (e.g. Semantic Web) Shared data or shared data services Physical challenges of largescale data Data access controls Where security and privacy come home to roost. 12

13 2020 Vision Discussion Topic LargeScale Matching Enterprise Service Bus (Secure, Standardsbased) Universal Translator) Workflow & Transaction Management Local Service Bus (standardsbased abstraction) Modular Matching Tier Internet Intelligence Cloud Computing Multibiometric Biometric Middleware MapReduce ScatterGather Biometric Indexing (!) Moore s Law Proprietary algorithms versus? Zero FTE Zero Errors Grid Computing Supercomputing models GPUbased matching 13

14 Designing for Capacity Not you average problem Average Capacity 2 x Average Capacity 3.5 x Average Capacity Entry Transactions processed per min in a typical IDENT day Entry Transactions processed per min in a typical IDENT day Entry Transactions processed per min in a typical IDENT day Queuing Delays Additional Time to process each transaction Queuing Delays Additional Time to process each transaction Queuing Delays Additional Time to process each transaction Designing for Average Capacity (Figures on left) illustrate without sufficient capacity, variables in volumes of day will cause significant queuing At 10:30 in the morning, when entry transactions exceed designed capacity, queuing builds up to as much as 8 hours and 42 minutes of delay. Queues are never cleared, and any workload carryover from the previous day adds to queuing problems of the next day Designing for 2 x Average Capacity (Middle Figures) will result in a 1 hour wait at Primary In the early afternoon, when number of entry transactions exceeds designed capacity per minute, queuing begins, and delays range from 752 mins. Although entry transaction volume drops beneath designed capacity at 5:30pm, delay impacts continue until 8:30pm due to accumulated queues Designing for 3.5 x Average Capacity (Figures on right) will result in acceptable wait times (seconds) at Primary 14

15 2020 Vision Discussion Topic Intelligence Enterprise Service Bus (Secure, Standardsbased) Universal Translator) Workflow & Transaction Management Local Service Bus (standardsbased abstraction) Modular Matching Tier Internet Intelligence Mission Perspective Comprehensive view of a person Linking of data to underlying facts Analytics Operational Perspective Performance data Biometric Accuracy monitoring Quality Assurance Capacity Management SLA management Concerns Data Mining Privacy Security 15

16 Contact Information Mark Crego Chief Architect, Accenture support of USVISIT (703) Michael Matyas Biometric Architect, Mount Airey Group (703)