Biometrics Crossover Error Rate
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and Segmentation Market Sizing & Forecasting Market and Product Strategy Consulting Marketing Campaign Support Strategic Advisory Sessions Resources Company Profiles White Papers About Overview Team Research Methodology Careers Contact Us Newsroom Press Releases Tractica in the News Events Blog My Reports Register June 23, 2015 Bob Lockhart In Biometrics, crossover error rate calculation Which Error Rate Matters? Biometrics error rates are like wine. The question, “What is a
Equal Error Rate Roc
good wine?” cannot be answered out of context. Not for me, at least. I need more information like, “What are you eating with equal error rate formula it?” I might not suggest the same wine for a rack of lamb as I would for a filet of sole. Biometrics’ wine question is, “What is the most important error rate?” Again, it depends on what you’re equal error rate matlab doing. Three error rates are commonly discussed relating to biometrics: the false accept rate, the false reject rate, and the equal error rate (or crossover error rate): the point where a biometrics’ false accept rate and false reject rates are equal. Vendors can tune their false accept and false reject rates: for example, by adjusting the size of their biometric templates or by adjusting the confidence interval that determines a match. Biometric verification is a statistical
Biometric Error Rates
process, not a yes/no comparison, so a more precise comparison produces a greater degree confidence in the verification. You might think that a greater confidence interval is better but that is not always the case. Like most everything in biometrics, error rate selection is governed by use cases. For starters, we can dismiss the equal error rate (EER). In all my research, I have yet to encounter a use case where having an equal probability of false accept or false reject was optimal. EER is an interesting academic construct but it’s hard to think of a use case where it matters. That leaves us with choosing the right false accept or false reject rate, and with choosing the right wine for our rack of lamb. A greater confidence interval lowers the probability of a false positive: that someone will be accepted who should not be accepted. This is highly desirable when authenticating a remote wire transfer of $10 million. It is the rack of lamb’s Châteauneuf-du-Pape. Low probability of false accepts is achieved by increasing the probability of false rejects, as shown in the figure below. Conversely, that high confidence level also increases the probability of false rejects – check out the red vertical line in that figure. For a convenience use case like smartphone authentication, too many false rejects can spell disaster, which also sp
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False Acceptance Rate Formula
_ Information Security Stack Exchange is a question and answer site for information security professionals. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask https://www.tractica.com/biometrics/in-biometrics-which-error-rate-matters/ a question Anybody can answer The best answers are voted up and rise to the top Determining the accuracy of a biometric system up vote 2 down vote favorite 1 How is the accuracy of a biometric security system (its ability to minimize false acceptance rate and false rejection rate) determined? authentication biometrics share|improve this question edited May 9 '14 at 11:22 Rory Alsop♦ http://security.stackexchange.com/questions/57589/determining-the-accuracy-of-a-biometric-system 50.6k1081256 asked May 9 '14 at 8:09 nuha 1113 I have removed the 2nd question you included - it is far too broad to be answerable here. –Rory Alsop♦ May 9 '14 at 11:23 add a comment| 3 Answers 3 active oldest votes up vote 3 down vote Well, I am not sure if this what you are looking for. In general, the performance of any biometric system (e.g fingerprint, voice, facial recognition, etc) is described using several metrics. FAR or False Acceptance rate is the probability that the system incorrectly authorizes a non-authorized person, due to incorrectly matching the biometric input with a template. The FAR is normally expressed as a percentage, following the FAR definition this is the percentage of invalid inputs which are incorrectly accepted. FRR or False Rejection Rate is the probability that the system incorrectly rejects access to an authorized person, due to failing to match the biometric input with a template. The FRR is normally expressed as a percentage, following the FRR definition this is the percentage of valid inputs which are incorrectly rejected. CER or Crossover Error Rate i
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