Overview of the Biometrics Lab at Purdue

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Overview of the Fall 2010 activity in the BSPA Lab at Purdue University. For more information, please go to www.bspalabs.org

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Overview of the Biometrics Lab at Purdue

  1. 1. Overview of the Biometrics Lab Learning | Engagement | Discovery<br />Biometric Standards, Performance, and Assurance Laboratory | <br />Purdue University<br /> www.bspalabs.org<br />www.twitter.com/bspalabs<br />www.slideshare.net/bspalabs<br />www.linkedin.com/companies/bspa-labs<br />
  2. 2. Applied Biometrics <br />The BSPA Laboratory was established in 2001 to meet the growing demand for applied research facilities in biometrics, primarily testing and evaluation<br />Mission: To excel in the applied research of biometric technologies with a continued commitment to education and innovative research, as well as engaging academia and industry in all of our activities.<br />
  3. 3. BSPA Lab Functional Areas<br />Over 10 years of experience in education: undergraduate, graduate, distance, and industrial settings. <br />
  4. 4. BSPA Lab Functional Areas<br />Capabilities:<br /><ul><li> Performance Assessment
  5. 5. Pilot testing
  6. 6. Technology, Scenario, Operational, Hybrid Testing
  7. 7. Usability Testing and Process Improvement</li></li></ul><li>BSPA Lab Functional Areas<br />Research focuses on solving issues that are observed in the field. Focus on:<br />- Performance, <br />- Identity management, <br />- Usability<br />
  8. 8. BSPA Lab Functional Areas<br />Participation:<br /><ul><li> International Representative & Head of Delegation for WG5 Testing
  9. 9. Editors of multiple standards</li></li></ul><li>BSPA Lab Functional Areas<br />These functional areas link into<br />Research<br />Education<br />Engagement / Outreach<br />
  10. 10. Learning<br />
  11. 11. Learning<br />Undergraduate and Graduate classes<br />Biometric Technologies certificate<br />CBP training<br />Graduate certificate in Biometrics*<br />Graduate courses <br />Offered both online and on-campus<br />
  12. 12. Undergraduate Course<br />Automatic Identification and Data Capture<br />Covers technology from bar codes, card technologies, and biometrics<br />No prerequisites<br />Offered online and offline<br />
  13. 13. Graduate Courses<br />
  14. 14. AIDC for the Enterprise<br />Graduate level course in Automatic Identification Technologies<br />Bar codes<br />Biometrics<br />RFID<br />Card technologies<br />
  15. 15. ICT Standards<br />1 credit course <br />Repeatable 3 times<br />Follows work in INCITS M1 and ISO/IEC JTC 1 SC37<br />Provides opportunities for students to provide technical comments<br />Closely linked to biometric standards work<br />
  16. 16. IT 55500 Biometric Technology Test Design, Performance, and Evaluation<br />An introduction of methods of designing biometric testing, performance, and evaluation analyses. <br />Methods of evaluating fingerprint, face, iris, and voice recognition data is explored using ROC curves, CMC, Rank statistics, and DET curves.<br />Examines testing requirements from submission of IRB documents to the final analysis.<br />Includes a component of comparative analysis within modalities.<br />
  17. 17. IT 54000 Biometric Performance and Usability Analysis <br />An introduction of test methodologies from disciplines outside of biometrics, which include:  <br />usability, <br />ergonomics, <br />human factors, <br />human-computer interaction, <br />Demonstrate how biometric data analysis can benefit from understanding how humans interact with biometric sensors during the testing and evaluation of biometric systems. <br />Explores test methods, case studies, and prior biometric testing reports in order to develop a test methodology that includes information on how users interact with biometric systems. <br />
  18. 18. IT 65700 Fingerprint Performance and Usability <br />Covers topics of fingerprint capture, fingerprint feature extraction, fingerprint matching, and attacks on fingerprint systems. <br />Requires analysis of real fingerprint data and the integration of fingerprint recognition in existing infrastructures.<br />Development of a fingerprint recognition system is required. <br />
  19. 19. IT 65800 Biometric Systems Interoperability: Applications and Challenges <br />Provides a technology neutral approach to the discussion of biometric system interoperability. <br />Examines the issues of biometric sub-systems of different biometric modalities and sub-systems of the general biometric model. <br />Students will be able to critically evaluate the impact of interoperability of sub-systems on the performance of the entire system. <br />
  20. 20. IT 54500 Biometric Technology and Applications<br />Foundation course<br />Six modules cover the IEEE CBP Body of Knowledge<br />Biometric Fundamentals<br />Biometric Modalities<br />Biometric System Design and Evaluation<br />Biometric Standards<br />Social, Cultural and Legal Implications<br />Biometric Applications<br />
  21. 21. Access to courses<br />Online – start at a time that is convenient to you<br />Learn at your own pace<br />For more information: http://www.bspalabs.org/about-us/contact-page/<br />
  22. 22. Applied Research<br />
  23. 23. Applied Research<br />Partnership Among Researchers, <br />Industry and Users and Standards Adoption (Podio, 2006) <br />
  24. 24. Our Approach – Applied Research through Testing<br />Blind (2008)<br />
  25. 25. Our Approach – Developing new devices<br />Blind (2008)<br />
  26. 26. Our Approach – Developing and Contribution to Standards<br />Blind (2008)<br />
  27. 27. Understanding Error <br />The development of our approach consists of:<br />Testing<br />Research<br />Standards<br />Education<br />
  28. 28. Research Agenda for Fall 2010<br />Biometric Operator Performance<br />Face Recognition and the Indiana Department of Correction<br />Standard Compliance of Legacy Biometric Data<br />Modeling biometric modalities onto the HBSI (Human Biometric Sensor Interaction) method<br />Understanding Biometric Error<br />Habituation….<br />
  29. 29. Biometric Operator Performance<br />Analyzing the impact of different instructional methods of training on a biometric data collection agent and how would that affect the biometric transaction times during operational environment.<br />Biometric modality: Mobile Iris Recognition<br />Two methods of training: <br />audio recording instruction<br />soundless video instruction<br />Two types of data collection agent from learning styles perspective:<br />Verbal learner (prefers written and spoken explanations)<br />Visual learner (prefers visual representations such as pictures, diagrams, flow charts)<br />
  30. 30. Face Recognition and the Indiana Department of Correction<br />Working with the Indiana Department of Correction to:<br />Review and Analyze Current and Legacy Mug Shots<br />Review current Mug Shot Capture Process<br />Utilize Analysis of mug shots and review of capture process to:<br />Propose an Optimized Capture Process<br />Capture mug shots with more consistency that are standard compliant<br />Implement Proposed Capture Process<br />Analyze Mug Shots from Proposed Capture Process<br />Determine if Proposed Process Successfully Optimized for Standard Compliance<br />
  31. 31. Standard Compliance of Legacy Biometric Data<br />Over time, government agencies collect a wide variety of face and fingerprint images from individuals. <br />Historically, this data was collected manually, and stored in a filing cabinet, or scanned into a digital format. <br />As agencies implement digital capture technologies, a question remains: what to do with the old data? <br />
  32. 32. Standard Compliance of Legacy Biometric Data<br />In this research project, we are analyzing face photographs that have been stored on paper, to examine whether these images are standard compliant. <br />This three year project examined over 48,000 digital and paper-based photographs from the Indiana Department of Correction, with the intent to develop a list of recommendations on how to deal with legacy data.<br />In this academic year, the results of the standard compliance analysis will be published, and available on the website.<br />
  33. 33. Modeling biometric modalities onto the HBSI (Human Biometric Sensor Interaction) method<br />The goal of this research is to provide the biometrics community with a comparative evaluation method for biometric devices that uses ergonomics, usability, biometric image quality, and traditional system performance criteria to evaluate the design and functionality of biometric devices and systems. <br />This model was initially developed using fingerprint recognition as a base modality, but as the model matures, we have started to map the other modalities onto the model. This academic year will see hand geometry and iris recognition mapped for model validation. <br />
  34. 34. Understanding Biometric Error<br />Historically, biometric performance has relied on basic metrics such as FNMR and FMR, as well as Failure to Enroll, Failure to Acquire etc. <br />As biometric deployments become widespread, and the number of people enrolled is in the millions, a 1% error rate is a significantly large number. <br />A large part of our research portfolio is trying to understand this error, and providing new definitions and metrics. <br />The goal of this research is to improve operational performance, design better systems (in line with the HBSI model), and to further the research communities understanding of biometric error. <br />
  35. 35. Habituation….<br />Inside the biometric community, the definition of the word habituation varies from person to person.  <br />The general concept of the word implies that something happens as the user repeatedly uses a system.  What this something is and the duration of repeatedly are the concern of this research.  <br />
  36. 36. Habituation…<br />Some argue that the number of errors committed by the user will decrease as the user becomes more comfortable with the system, others argue that once comfortable with the system, the user’s interaction will become sloppy, therefore increasing the number of errors occurring.  <br />Others would like to quantify habituation as involving performance or image quality as opposed to error rates.  <br />No matter which side of the fence a researcher sits, there is a need to either accept an overarching definition of habituation or discard the word for a framework of variables<br />
  37. 37. Habituation…<br />Industrial engineering has long used the term habituation, but has an accepted definition of the word.  <br />This research will look at the definitions and changes in the use of the word habituation from both fields over time.  <br />Additionally, this study will try to create a framework in an attempt to create a cohesive model for the concept coined habituation.  To do this, the study will utilize previous research and findings from multiple fields and sources.<br />
  38. 38. Engagement | Outreach<br />
  39. 39. Engagement | Outreach Mission<br />Part of our mission is to engage and outreach to the biometrics community<br />Successful deployment of biometrics is crucial to the growth of the biometrics industry<br />Engage with companies to test, evaluate biometric devices<br />
  40. 40. Engagement | Outreach Mission<br />Engage with industry to solve complex problems<br />Contribute to standards activities, both in the U.S. and internationally<br />
  41. 41. Opportunities for Students<br />
  42. 42. Opportunities for students<br />Of those that graduate from the lab<br />100% placement<br />Work for government, contractors, private sector firms<br />Many students have internships<br />
  43. 43. Companies where students have interned or accepted full time jobs<br />
  44. 44. How to contact us<br />Knoy Hall of Technology, Purdue University<br />401 N. Grant Street<br />West Lafayette, IN 47907-2021<br />www.bspalabs.org<br />(765) 495-2311<br />contact@bspalabs.org<br />

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