Biometrics Revisited

Click here to receive your FREE subscription to Campus Technology

Home > Biometrics Revisited

Security :: Biometrics

Biometrics Revisited

1/1/2008

By Doug Gale

Higher Ed Experiences

Although not widespread, the use of biometrics for identifi- cation is growing in higher education, with applications broadly grouped into two categories: 1) low-security "time and attendance," and 2) access to relatively high-security resources. Examples of the former include the use of fingerprint readers to log hours worked by students in the IT department at the University of Kansas and a similar use at Gannon University (PA) where, according to John Crandall, associate director of information technology services, the school has been using a fingerprint identification product from AIG Technology to replace paper timecards for 60 hourly employees in two locations.

The Algorithm Is Mightier Than the Chip

THE ALGORITHMS USED to parameterize biometric information are steadily improving. Periodically, the National Institute of Standards and Technology conducts large-scale performance tests on biometric technologies. The Face Recognition Vendor Test (FRVT) 2006 results show that performance has been steadily improving-more than an order of magnitude in the last four years alone, as shown on the chart below. The graph reveals the decrease in false rejection rates between 1993 and 2006, at a constant false acceptance rate of 0.1 percent.

Security There is more to the story, however. These results were obtained under controlled illumination, and performance varied between vendors. While there were similar improvements in performance under varied lighting conditions, under those parameters the FRR results of the 2006 evaluations ranged from 0.1 to 0.4. Stated differently, between 10 and 40 percent of the subjects were rejected falsely in uncontrolled lighting. Bottom line? It's still hard to pick out a face in the crowd.

The latter system took about two person-weeks of effort to install, spread over a six-week period, with much of that work centered around writing the middleware to interface the product to the institution's payroll system. Initially, senior management had privacy concerns. But then it was explained that the system did not store an image of an individual's fingerprint but, rather, a template based on the fingerprint's characteristics. That was important, as the templates were stored on a centralized server located in the university's data center.

In practice, Gannon has had some problems with false rejections, which usually have been the result of lotion on an employee's hands, or residue on the sensor.