5 It was once called false acceptance by biometrics researchers and vendors, a term that has been more recently been replaced by the term false match. This class of error is known as a type-2 error by statisticians. This might happen if the user Alice tries to authenticate as the user Bob, for example. In this case, random errors occur that let a user be erroneously recognized as a different user. In the second case, a decision subsystem incorrectly returns a yes instead of a no. One way in which the accuracy of biometric systems is now typically quantified is by their false nonmatch rate (FNMR), a value that estimates the probability of the biometric system making a type-1 error in its decision subsystem. It was once called false rejection by biometrics researchers and vendors, a term that has more recently been replaced by the term false nonmatch.
This class of error is known as a type-1 error by statisticians, 4 a term that would almost certainly be a contender for an award for the least meaningful terminology ever invented if such an award existed. This type of error might result in the legitimate user Alice inaccurately failing to authenticate as herself. In this case, a user is indeed who she claims to be, but large random errors occur in the data capture subsystem and cause her to be incorrectly rejected. In one case, a decision subsystem makes the incorrect decision of no instead of yes. There are two general types of errors that can occur. Comparison scores close to the average that result in a yes decision.Įrrors may occur in any decision subsystem. If the threshold is increased, the gray area will get wider and increase in size so that more comparison scores result in a yes answer.įigure 6.7.
If the threshold is decreased, the size of the gray area will get narrower and decrease in size so that fewer comparison scores result in a yes answer. In Figure 6.7, the threshold value defines how far the gray area extends from the central average value. Comparison scores in the gray area of this illustration are close to the average value and result in a yes, whereas comparison scores that are outside the gray area are too far from the average value and result in a no. Comparison scores that will result in a yes or no response from a decision subsystem are shown in Figure 6.7. If the comparison score is greater than the threshold, it returns the value no.
If the comparison score is less than or equal to the threshold value then the decision subsystem returns the value yes. The threshold value represents a measure of how good a comparison needs to be to be considered a match. To make a yes or no decision, a decision subsystem compares a comparison score with a parameter called a threshold.
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