1. Facial Recognition at Purdue Universitys 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. Identifying Individuals There are three common ways of distinguishing someones 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. 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. 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. 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. Experimental Setup Purdue University 2006 6

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. 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. 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. 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. 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. 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. 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. 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. 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 users 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. 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. 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