Facial Recognition at Purdue
                            University’s Airport
                                2003-2008

 ...
Identifying Individuals

             • There are three common ways of distinguishing
               someone’s identity
  ...
Introduction
             •      Physiological biometrics include:
                      –    Facial recognition
         ...
Motivation

             • Cross Disciplinary Research Opportunity with
               Aviation Technology
             • ...
Experimental Setup

             • Evaluation takes place in Hanger 6, the
               Student Flight Operations Center...
Experimental Setup




© Purdue University 2006
                                                6
Experimental Setup

             • The face recognition system was setup in the Hanger 6
               area with no addit...
Volunteer Crew


             • Recruitment of the students occurs at the
               flight operations safety briefing...
Cohorts

             • Cohorts are used to describe the groupings of
               students
                      – Curr...
Enrollment

             • Each enrollment required an individual to stand in front
               of the camera, and move...
Initial Results from the Study

             • As the data is collected in a 1:M classification
               mode, ie: t...
Initial Results

             • Classify
                      – The classification rate in a 1:M scenario is currently
  ...
Issues with 1:M operational testing

             • The result of a classification failure in a 1:M
               situati...
Issues with Operational Testing

             • The false non-match rate used in this
               evaluation is defined...
Issues with Operational Testing

             • Failure to Acquire
                      – The failure to acquire rate is ...
Issues with Operational Testing

             • Unknowns
                      – An unknown is also a classification failu...
Issues and Conclusions with Operational Testing and the
                  Reporting of Results
             • The issue wi...
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(2003) Facial Recognition at Purdue University Airport

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This presentation outlined preliminary work in testing and evaluating face recognition at Purdue University airport.

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(2003) Facial Recognition at Purdue University Airport

  1. 1. Facial Recognition at Purdue University’s Airport 2003-2008 Jeremy M. Morton, C. Michael Portell, Stephen J. Elliott, Ph.D. & Eric P. Kukula Presented By: Eric Kukula Biometric Standards, Performance, and Assurance Laboratory www.biotown.purdue.edu Department of Industrial Technology, School of Technology, Purdue University, West Lafayette, IN 47906 © Purdue University 2006 1
  2. 2. Identifying Individuals • There are three common ways of distinguishing someone’s identity – Through something that an individual knows • A password – Something an individual has • Identification card – Something they own • Biometric • In many airport applications, individuals gain access to specific areas by providing a card and personal identification number (PIN). – A combination of these identifiers provides a more robust security option. • Biometric identification is defined as: – The “automatic identification or identity verification of (living) individuals based on behavioral and physiological characteristics” © Purdue University 2006 2
  3. 3. Introduction • Physiological biometrics include: – Facial recognition – Finger – Face – Eye – Hand • Behavioral biometrics include: – Speaker (voice) – Keystroke – Dynamic signature verification. • Furthermore, a biometric must be: – Measurable – Robust – Distinctive • Within the research community, there is interest in the performance of a biometric over an extended period of time. • According to Wayman template aging is defined as: – “the increase in error rates caused by time related changes in the biometric pattern”, and that “longer time intervals generally make for more difficulty in matching samples to templates” – Furthermore, a study of template aging will also require a cohort of participants who are available over an extended period of time. – In this study, the time period will last up to five years. © Purdue University 2006 3
  4. 4. Motivation • Cross Disciplinary Research Opportunity with Aviation Technology • The existence of an airport facility on campus, with a potential cohort of individuals to participate over a five year program – Flight students – Faculty – Staff • Interdisciplinary relationships with other departments • Need to understand how images change over a period of time © Purdue University 2006 4
  5. 5. Experimental Setup • Evaluation takes place in Hanger 6, the Student Flight Operations Center at the airport • Evaluation of the area completed in conjunction with a graduate class in Biometric Technology • Constraints: – Had to fit in with the existing operation of the airport – Had to be as unobtrusive as possible – Had to be able to collect data ongoing for a 5 year period, therefore setup of camera was critical – In line with the labs philosophy • Therefore commercially available software had to be used, with all images stored © Purdue University 2006 5
  6. 6. Experimental Setup © Purdue University 2006 6
  7. 7. Experimental Setup • The face recognition system was setup in the Hanger 6 area with no additional environmental controls. • A Logitech QuickCam Pro 4000 was used. This camera has a video resolution of 640x480 VGA CCD, with up to 30 frames per second. • The camera also has a still image capture of up to 1280 x 960 pixels, 1.3 mega pixels. • The video camera was connected to Dell Omniplex GX260 2.0 GHZ, 512 MB RAM computers through a 35ft cable with USB boosters. • The camera was 6 inches off the table. The angle of the camera also accommodated all of the participants, regardless of their height. As lighting was constant, it was not measured continually. © Purdue University 2006 7
  8. 8. Volunteer Crew • Recruitment of the students occurs at the flight operations safety briefings – Held once per month. • It is anticipated that over 300 students will be enrolled over the duration of the test. • Currently in Cohorts 1 and 2 © Purdue University 2006 8
  9. 9. Cohorts • Cohorts are used to describe the groupings of students – Currently 2 cohorts • So far there are 2 cohort groups – Group 1 • 71 individuals – 60 males – 11 females – Enrolled in a 4 day period in May 2003 – Group 2 • 56 individuals – 54 male – 2 female © Purdue University 2006 9
  10. 10. Enrollment • Each enrollment required an individual to stand in front of the camera, and move their head to the left, right, up and down. • 100 images were taken for each enrollment, an enrollment taking approximately 1.5 minutes • The experimenters enrolled people in batches, so later enrollees were more habituated to the enrollment process than those at the beginning of the enrollment period. • It was noticed that sometimes, in a batch enrollment, people would speed up their movement, causing the FRS system to loose track of the individual – This was the only significant issue with enrollment • There was 0% Failure to Enroll (FTE) • There was also a 0% Failure to Acquire (defined as successfully acquiring 100 images). © Purdue University 2006 10
  11. 11. Initial Results from the Study • As the data is collected in a 1:M classification mode, ie: there is no claim to identity, each image collected by the system has to be manually verified against its original template • The system returns two different variables – Classify – Classify Failure © Purdue University 2006 11
  12. 12. Initial Results • Classify – The classification rate in a 1:M scenario is currently between 70-73% – We are now seeing a decline in the classification success rate and an increase in the classification failure rate due to the following reasons: • It is a 1:M system, so the user is not presenting their token or password to be verified as a 1:1 match. – Therefore, when an individual walks by the camera, the system takes an image, and compares it to all known individuals in the database • However, there are lessons to be learned regarding the testing and evaluation of biometric equipment in operational tests © Purdue University 2006 12
  13. 13. Issues with 1:M operational testing • The result of a classification failure in a 1:M situation may be an understatement of the true performance of the FRS system due to: – A False Match – A Failure to Acquire – An unknown person © Purdue University 2006 13
  14. 14. Issues with Operational Testing • The false non-match rate used in this evaluation is defined as the error rate of the matching algorithm from a single attempt- template comparison in a genuine attempt. • This rate will be established by the research team using additional software currently being developed © Purdue University 2006 14
  15. 15. Issues with Operational Testing • Failure to Acquire – The failure to acquire rate is the proportion of attempts for which the system is unable to capture or locate the user’s face – In this situation, as the camera is in a high traffic area, the camera cannot find a face, therefore times out – The failure to acquire rate is counted as a classification failure © Purdue University 2006 15
  16. 16. Issues with Operational Testing • Unknowns – An unknown is also a classification failure. An unknown is defined for this study as someone who has not yet been entered into the system – The FRS captures an image, tries to match that image with all those enrolled in the database, yet fails to find one – It then returns classification failure © Purdue University 2006 16
  17. 17. Issues and Conclusions with Operational Testing and the Reporting of Results • The issue with operational testing is that the commercially available software (COTS) used in this study overstates the classification failure rate due to the issues discussed: – False Match – Failure to Acquire – Unknowns • Research is currently underway at the Biometrics Standards, Performance, and Assurance Laboratory to resolve these issues, and provide a comprehensive best practice testing and reporting guide for 1:M applications © Purdue University 2006 17

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