Evaluating Biometric System

Topics: Biometrics, Facial recognition system, Fingerprint recognition Pages: 18 (5288 words) Published: February 24, 2013
An Introduction to Evaluating Biometric Systems

How and where biometric systems are deployed will depend on their performance. Knowing what to ask and how to decipher the answers can help you evaluate the performance of these emerging technologies.

P. Jonathon Phillips Alvin Martin C.L. Wilson Mark Przybocki National Institute of Standards and Technology


n the basis of media hype alone, you might conclude that biometric passwords will soon replace their alphanumeric counterparts with versions that cannot be stolen, forgotten, lost, or given to another person. But what if the performance estimates of these systems are far more impressive than their actual performance? To measure the real-life performance of biometric systems—and to understand their strengths and weaknesses better—we must understand the elements that comprise an ideal biometric system. In an ideal system • all members of the population possess the characteristic that the biometric identifies, like irises or fingerprints; • each biometric signature differs from all others in the controlled population; • the biometric signatures don’t vary under the conditions in which they are collected; and • the system resists countermeasures. Biometric-system evaluation quantifies how well biometric systems accommodate these properties. Typically, biometric evaluations require that an independent party design the evaluation, collect the test data, administer the test, and analyze the results. We designed this article to provide you with sufficient information to know what questions to ask when evaluating a biometric system, and to assist you in determining if performance levels meet the requirements of your application. For example, if you plan to use a biometric to reduce—as opposed to eliminate— fraud, then a low-performance biometric system may be sufficient. On the other hand, completely replacing

an existing security system with a biometric-based one may require a high-performance biometric system, or the required performance may be beyond what current technology can provide. Here we focus on biometric applications that give the user some control over data acquisition. These applications recognize subjects from mug shots, passport photos, and scanned fingerprints. Examples not covered include recognition from surveillance photos or from latent fingerprints left at a crime scene. Of the biometrics that meet these constraints, voice, face, and fingerprint systems have undergone the most study and testing—and therefore occupy the bulk of our discussion. While iris recognition has received much attention in the media lately, few independent evaluations of its effectiveness have been published.

There are two kinds of biometric systems: identification and verification. In identification systems, a biometric signature of an unknown person is presented to a system. The system compares the new biometric signature with a database of biometric signatures of known individuals. On the basis of the comparison, the system reports (or estimates) the identity of the unknown person from this database. Systems that rely on identification include those that the police use to identify people from fingerprints and mug shots. Civilian applications include those that check for multiple applications by the same person for welfare benefits and driver’s licenses. In verification systems, a user presents a biometric signature and a claim that a particular identity belongs to the biometric signature. The algorithm either accepts 0018-9162/00/$10.00 © 2000 IEEE



or rejects the claim. Alternatively, the algorithm can return a confidence measurement of the claim’s validity. Verification applications include those that authenticate identity during point-of-sale transactions or that control access to computers or secure buildings. Performance statistics for verification applications differ substantially from those for identification systems. The main...

References: 1. J.P. Egan, Signal Detection Theory and ROC Analysis, Academic Press, New York, 1975. 2. A. Martin et al., “The DET Curve Assessment of Detection Task Performance,” Proc. EuroSpeech 97, IEEE CS
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