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It should be no surprise that most biometric testing centers around measuring the correctness, or accuracy, of system decisions determining experimentally how often they are right Biometric systems all ultimately produce a binary decision output By definition, the output is yes or no (accept or reject) For each output, a corresponding error can occur These are classically referred to as type I and type II errors Type I errors occur when the positive hypothesis (true condition) is incorrectly rejected the system should have said yes, but instead returned no Type II errors are when the negative hypothesis (false condition) is incorrectly accepted the system should have said no, but instead returned yes Biometric testing literature discusses accuracy in terms of the likelihood of these types of errors; however, current terminology refers to type I errors as the False Reject Rate (FRR) and type II errors as the False Accept Rate (FAR) To keep the biometric cognoscenti on their toes, a third notation is also used FRR is sometimes called False Non-Match Rate (FNMR) and, likewise, FAR is sometimes referred to as False Match Rate (FMR) (And to completely confuse anyone remotely interested in biometric testing, but to make our list of error-rate terms complete, False Alarm Rate is sometimes used for False Accept Rate) The FRR and FAR for a biometric system always depend on the match threshold and are always inversely related The match threshold is an empirically determined value such that all match scores greater than or equal to this value are considered positive matches for a given system As the match threshold is increased, it becomes less likely an imposter will be falsely accepted At the same time, however, the likelihood that valid users will be falsely rejected increases For a given system, it is not possible to reduce both error rates simultaneously The improvement of one is obtained only at the cost of the other Depending on the purpose of the system, one type of error might be preferred over the other The best setting is a result of balancing user convenience (few false rejects) with security objectives (few false accepts), and carefully considering the costs and risks of each error type in context.

Also, Gc (G1)(G2)(G3) (E-5)

Best practices suggest that match decision error rates be presented as plots characterizing the error trade space because the threshold might be varied over its entire range for that system The relationship between the match rate and the

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Not only are subtotals shown by default, but the PivotTable also shows grand totals by default These appear at the bottom and right of the PivotTable but can be turned off as part of setting properties for the entire PivotTable Right-clicking anywhere in the PivotTable and then choosing PivotTable Options opens the PivotTable Options dialog box There are many properties that can be altered via this dialog box, but grand totals are found on the Totals & Filters tab Here, two check boxes control the row and column grand totals so they can be turned on and off independently Figure 9-12 shows this dialog box with both the row and column grand totals turned off The PivotTable reflects these changes, and also moves the subtotals to the bottom of the group as described in the previous paragraph

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role Unless organizations can deploy and administer the systems economically, the systems won t be used In authentication systems, the important features are enrollment, maintenance, revocation, and the handling of operational problems To a large extent, these features take on particular properties depending on the authentication factors being used

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Excel 2007 includes additional formatting capabilities that allow end users to apply conditional formatting to their Excel spreadsheets One such feature is conditional

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