Multimodal Biometrics R&D Efforts to Exploit Biometric Transaction Management Systems

Transcription

1 Multimodal Biometrics R&D Efforts to Exploit Biometric Transaction Management Systems Erik J Bowman Principal Technologist Advanced Technology Group 24 September 2009

2 Agenda Problem Statements Research Scope Research Topics Biometric Transaction Management Systems Business Monitoring Benchmarking Activities Face Recognition Prototype 2

3 Problem Statements 1. Customers are requiring increased accuracy and faster throughput 2. Data sets are getting larger as national identity schemes proliferate 3. Customers are seeking additional biometric modalities 4. Customers prefer open standards across the entire system 5. Customers want the system to be transparent 3

4 Research Scope Program Overview The Strategic Identity Performance Platform (SIPP) is a multi-year R&D program investing in furthering architectures supporting large-scale identification programs Develop independent and large-scale operational capabilities Demonstrate federated searching capability to query the system in a secure manner Utilization of appropriate web service standards to achieve plug-and-play performance Develop secure and verifiable means for long term protection of biometric data repositories Biometric Reference Architecture Capabilities/Discriminators Delivered Large-scale independent biometric test bed 1 Million 10 Print Records 80,000 Palm Records 600K Face Images from various sources 3,000 Iris Images from various sources Biometric Fusion Research Score Level Fusion Unimodal and Multimodal Developing Operational Scenarios in which to implement biometric fusion Intelligent Workflow Smart Routing Based on Intermediate Data (e.g. quality) 4 Technologies Employed

5 Research Topics 1. Large-scale Facial Recognition Goal: Provide performance results based on a large repository of diverse faces Problem Solved: Determine accuracy of COTS providers Status: Over 5,000 inputs and nearly 600K gallery, testing with COTS providers (e.g. Cognitec, L-1, Sagem, etc.) 2. Large-scale 10 Print to 10 Print Accuracy Goal: Provide performance results based on a large repository of fingerprints and palms Problem Solved: Lights out matching, integration approaches Status: 13,000 input sets 2P, 4P, 10P, on-going integration with leading COTS providers (BioKey, L-1, Sagem, NEC, Cogent, etc) 3. Face Quality Assessment Goal: Provide a statistical model to predict accuracy score given certain quality parameters (for use with static images and surveillance footage) Problem Solved: Which parameters of a given photo matter (e.g. inner eye distance) Status: On-going research studying COTS providers parameters 4. Biometric and Information Fusion Goal: Determine proper mix of fusion approaches for operational scenarios Problem Solved: Provide higher confidence in matching results Status: On-going research studying various fusion approaches (e.g. Borda Count, Weighted Sum, Bayes, etc.) 5. Non-Ideal Biometric Capture & Pre-Processing Developing unique approaches to predicting precise moment to capture samples such as a face or an iris Exploiting all modalities (face, voice, iris, etc) to understand what and how to improve the sample for matching 5

6 Biometric Transaction Management Requestors input data in a standard format in Parsing and scheduling of the transaction occurs Searching and matching events are executed Integration can happen at different levels 6

7 What is in a Biometric Workflow? A typical biometric workflow provides an infrastructure for: Handling the complexities of biometric transactions Providing a framework to support multimodal biometric fusion Enabling plug and play biometric architecture Supporting necessary response and transaction throughput time Biometric transactions are different then financial transaction: Long running: Can span minutes, hours, days, etc Large payload: > 500 KB Example of a single biometric transaction: Potential to be much larger depending on the content 7

8 2007 Benchmark Criteria Participating Vendors and products evaluated in NG s BPEL benchmark Oracle BPEL Process Manager (10.1.3) with Oracle SOA Suite Application Server BEA WebIntegration with BEA Weblogic Application Server IBM Process Manager with WebSphere Application Server Products were configured to sustain a 20,000 transaction load invoking a BPEL process Evaluation Criteria Included: Open Standards Performance Scalability 8

9 Findings - Benchmark BEA benchmark test was not completed Vendor was not able to set up the load test with JMeter Only single transaction could be processed IBM and Oracle products where able to initiate a 20,000 transaction load No transactions were lost All products used DB adapters to update the log table IBM and Oracle both encountered DB connection faults Connection faults were attributed to proper tuning of the products to handle the # of database connection Tuning is non-trivial Non Typical environment affected testing JMeter application used to produce the load and collect results was on the same machine as the Application server Persistence database/dehydration store on the same server as the application database 9

10 Business Monitoring Business Monitoring provides the capability to analyze and predict system resource usage Enables the IT professional to optimize the system and it s resources Two products implemented for transaction activity monitoring: JFREE Chart (Open Source) Java and SQL based Predefined charts WebSphere 6.2 Business Monitor/Business Space Provides the business user an interactive dashboard for business process monitoring, including predictive analysis based on key performance indicators KPI s 10

11 Biometric Business Space Dashboard Alert KPI Instances 11

12 Benchmarking Activities Print to Print Accuracy Still the primary biometric for identification on large-scale programs 13K inputs --> card scan slap images 18K mates --> card scan rolled images 1M ten print card scan records in gallery New Algorithms for 4x4 are 99.6% TAR at 0.3% FAR compared to a 2% FAR at a similar TAR Face matching accuracy is getting far better on large data sets False alarm rates are decreasing Being used a second biometric, or tie breaker on large-scale systems Increased interest in facial recognition in video screening applications 12

13 Video Surveillance / Face Recognition Objectives Prototype RFID/Facial Recognition Application o Install and operate two configurations of application, integrating RFID and facial recognition, one fixed installation at a hightraffic location on-board and one installation at the gangway o Deploy and operate crew enrollment configuration o Test and obtain operational data from the shipboard installations Operating Environment Test Facial Clustering Application o Test and obtain operational data from the shipboard installation Overall Results Events, both the RFID/FR and Guest Services FR apps o 150,164 Events (Searches, matches, tag reads) o 87,730 Video Frames with Faces o 9 Camera Positions o 1,527 people enrolled in the gallery o Socializing the project is key to success Very Poor Quality Enrollment Photos o 370 of the Crew were re-enrolled throughout the week capturing higher quality pictures o At an 80% True Positive Rate with the old photos we observe a 14.3% False Positive Rate & with the new photos, we have a 6.1% False Positive Rate a 57.34% increase in the ID rate o Inner Eye Distance counts o > 50 best and > 70 is optimal o The more pixels between the eyes, the more reliable the match 13 Performance Results (Accuracy) Day 2 Day 5 Better Performance as the week progressed

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