Editing Biometrics (section)

==Performance==
The discriminating powers of all biometric technologies depend on the amount of [[Entropy (information theory)|entropy]] they are able to encode and use in matching.<ref>{{cite journal |last1=Daugman |first1=John |title=Collision Avoidance on National and Global Scales: Understanding and Using Big Biometric Entropy |date=24 February 2021 |doi=10.36227/techrxiv.14061671 |url=https://www.cl.cam.ac.uk/~jgd1000/BiomEntropy.pdf}}</ref> 
The following are used as performance metrics for biometric systems:<ref>{{cite web|url=http://www.ccert.edu.cn/education/cissp/hism/039-041.html|title=Characteristics of Biometric Systems|publisher=Cernet|url-status=dead|archive-url=https://web.archive.org/web/20081017165633/http://www.ccert.edu.cn/education/cissp/hism/039-041.html|archive-date=17 October 2008}}</ref>
* '''False match rate''' (FMR, also called FAR = False Accept Rate): the probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs that are incorrectly accepted. In case of similarity scale, if the person is an imposter in reality, but the matching score is higher than the threshold, then he is treated as genuine. This increases the FMR, which thus also depends upon the threshold value.<ref name=Choubisa />
* '''False non-match rate''' (FNMR, also called FRR = False Reject Rate): the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs that are incorrectly rejected.
* '''[[Receiver operating characteristic]]''' or relative operating characteristic (ROC): The ROC plot is a visual characterization of the trade-off between the FMR and the FNMR. In general, the matching algorithm performs a decision based on a threshold that determines how close to a template the input needs to be for it to be considered a match. If the threshold is reduced, there will be fewer false non-matches but more false accepts. Conversely, a higher threshold will reduce the FMR but increase the FNMR. A common variation is the ''Detection error trade-off (DET)'', which is obtained using normal deviation scales on both axes. This more linear graph illuminates the differences for higher performances (rarer errors).
* '''Equal error rate''' or crossover error rate (EER or CER): the rate at which both acceptance and rejection errors are equal. The value of the EER can be easily obtained from the ROC curve. The EER is a quick way to compare the accuracy of devices with different ROC curves. In general, the device with the lowest EER is the most accurate.
* '''Failure to enroll rate''' (FTE or FER): the rate at which attempts to create a template from an input is unsuccessful. This is most commonly caused by low-quality inputs.
* '''Failure to capture rate''' (FTC): Within automatic systems, the probability that the system fails to detect a biometric input when presented correctly.
* '''Template capacity''': the maximum number of sets of data that can be stored in the system.