# In biometric what are roc curves useful for?

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Video answer: Roc and auc, clearly explained!

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A receiver operating characteristic (ROC) curve can be used to **evaluate the performances of algorithms in many biometric applications** and especially in the applications of analyzing fingerprint data on large data sets.

Video answer: Wizard for mac: about roc curves

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ROC curves are used in clinical biochemistry to choose the most appropriate cut-off for a test. The best cut-off has the highest true positive rate together with the lowest false positive rate.

Using the ROC curve we can pick a probability threshold that matches our interests for an specific task, in order to avoid certain errors over others. ROC is not only a way to compare algorithms, but it lets us pick the best threshold for our classification problem depending on the metric that is most relevant to us.

ROC Curve is an effective method of evaluating the quality or performance of diagnostic tests, and is widely used in radiology to evaluate the performance of many radiological tests.ROC curve view the full answer

ANSWER GIVEN BY ROC Curve is an effectual method of evaluating the eminence or performance of indicative tests, and is widely used in radiology to evaluate the presentation of many radiological t view the full answer

A Receiver Operator Characteristic (ROC) curve is a graphical plot used to show the diagnostic ability of binary classifiers. It was first used in signal detection theory but is now used in many other areas such as medicine, radiology, natural hazards and machine learning.

ROC Curve is a useful tool to compare classification methods and decide which one is better. Suppose a computer algorithm is implemented to diagnose a medical condition. Using ROC curves, we can compare its performance against a doctor's diagnosis, and against doctor's diagnosis when aided with computer-assisted detection (CAD).

Fig. 1 — Some theoretical ROC curves AUC. While it is useful to visualize a classifier’s ROC curve, in many cases we can boil this information down to a single metric — the AUC.. AUC stands for area under the (ROC) curve.Generally, the higher the AUC score, the better a classifier performs for the given task.

Which is the right way to plot an ROC curve for biometric verification? - Cross Validated. Receiver operating characteristics (ROC) curves provide critical performance insights for the evaluation of an authentication algorithm. However, there are two different versions of it in literature I'm not sure which one to follow.

the plot corresponds to the best biometric performance. • ROC graph (Receiver Operating Characteristic) plots true positive (1 FRR) vs. false positive rate (FAR). Best biometric performance near the top of the plot. DET curves are generally far better at highlighting areas of

The receiver operating characteristics (ROC) curve has been extensively used for performance evaluation in multimodal biometrics fusion. However, the processes of fusion classifier design and the final ROC performance evaluation are usually conducted separately.

Finally, ROC curves are particularly useful for comparing the discriminatory capacity of diﬀerent potential biomarkers. For example, if for each value of speciﬁcity one marker always has a higher sensitivity, then this marker will be a uniformly better diagnostic measurement. See Zhou, McClish, and Obuchowski(2002)orPepe(2003)formorediscussionofROC