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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.
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Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.
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This equation affords a convenient accuracy check when constructing the derivatives of rational B-splines. Implementation of the Procedure. The systematic procedure for implementing the rational B-spline approach is very similar to the scheme for applying B-splines described earlier. The process is as follows, assuming the motion constraints have already been established: 1. 2. 3. 4. Select the appropriate order of the rational B-splines to be used. Choose the weight sequence. Establish a knot sequence. Determine the values of each rational B-spline or rational B-spline derivative at all points where motion constraints are imposed. 5. Collect the values of rational B-splines and/or rational B-spline derivatives and form a linear system of equations using the motion constraints in Eqs. (5.11) and (5.12). 6. Solve the solution of the linear system equations formed in the step above for the coef cients Aj. 7. Evaluate rational B-splines and/or their derivatives as needed to determine displacement and/or its derivatives between motion constraints using Eqs. (5.11) and (5.12). Example Applications. The examples that follow illustrate the application of rational Bsplines to the synthesis of the rise portion of a DRD motion program. The cases presented illustrate the effects that adjustments to the rational B-spline parameters have on the synthesized motion programs. Note that in the cases below, normalized values for both displacement and time are used to provide a convenient basis for comparison of results. Accordingly in the examples Sc denotes a normalized value for cam displacement and S the follower output motion dis-
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