Fall 2010 Research Projects Learning | Engagement | Discovery<br />Biometric Standards, Performance, and Assurance Laborat...
Applied Biometrics <br />The BSPA Laboratory was established in 2001 to meet the growing demand for applied research facil...
Student Research Projects for Fall 2010(excludes funded research projects)<br />Biometric Operator Performance<br />Face R...
Biometric Operator Performance<br />Analyzing the impact of different instructional methods of training on a biometric dat...
Face Recognition and the Indiana Department of Correction<br />Working with the Indiana Department of Correction to:<br />...
Standard Compliance of Legacy Biometric Data<br />Over time, government agencies collect a wide variety of face and finger...
Standard Compliance of Legacy Biometric Data<br />In this research project, we are analyzing face photographs that have be...
Modeling biometric modalities onto the HBSI (Human Biometric Sensor Interaction) method<br />The goal of this research is ...
Understanding Biometric Error<br />Historically, biometric performance has relied on basic metrics such as FNMR and FMR, a...
Habituation….<br />Inside the biometric community, the definition of the word habituation varies from person to person.  <...
Habituation…<br />Some argue that the number of errors committed by the user will decrease as the user becomes more comfor...
Habituation…<br />Industrial engineering has long used the term habituation, but has an accepted definition of the word.  ...
How to contact us<br />Knoy Hall of Technology, Purdue University<br />401 N. Grant Street<br />West Lafayette, IN 47907-2...
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Research fall 2010

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Research fall 2010

  1. 1. Fall 2010 Research Projects 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. Student Research Projects for Fall 2010(excludes funded research projects)<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 />
  4. 4. 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 />
  5. 5. 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 />
  6. 6. 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 />
  7. 7. 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 />
  8. 8. 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 />
  9. 9. 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 />
  10. 10. 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 />
  11. 11. 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 />
  12. 12. 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 />
  13. 13. 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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