Review of several false positive error rate estimates for latent fingerprint examination proposed based on the 2014 Miami Dade Police Department study (2024)

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Review of several false positive error rate estimates for latent fingerprint examination proposed based on the 2014 Miami Dade Police Department study? ›

In 2014, the Miami Dade Police Department (MDPD) Forensic Services Bureau conducted research to study the false positive error rate associated with latent fingerprint examination. They report that approximately 3.0% of latent fingerprint examinations result in a false positive conclusion.

A false positive error occurs when an examiner concludes that two prints share a common source when, in fact, they do not. This error is captured by judgments in cell B. A false negative error occurs when an examiner concludes that two prints do not share a common source when, in fact, they do.

In this study, 125 fingerprint agencies completed a mandatory proficiency test that included two pairs of CNMs. The false-positive error rates on the two CNMs were 15.9% (17 out of 107, 95% C.I.: 9.5%, 24.2%) and 28.1% (27 out of 96, 95% C.I.: 19.4%, 38.2%), respectively.

In the modern age, there still is not a definitive certainty in how unique the match between fingerprints are. It was claimed that a false positive was one in 64 million. In one study, researchers found fingerprint exams had a false positive error rate of 0.1% and a false negative rate of 7.5%.

Five examiners made false positive errors for an overall false positive rate of 0.1%. Eighty-five percent of examiners made at least one false negative error for an overall false negative rate of 7.5%.

A test result that indicates that a person has a specific disease or condition when the person actually does not have the disease or condition.

A false positive error, or false positive, is a result that indicates a given condition exists when it does not. For example, a pregnancy test which indicates a woman is pregnant when she is not, or the conviction of an innocent person.

FRR is calculated by dividing the number of false rejects by the total number of transactions. When you have a low FRR, it means that your biometric system is rejecting fewer people than it should be.

During the past decade, several studies have been conducted to estimate the false positive error rate (FPR) associated with latent fingerprint examination. The so-called Black-box study by Ulery et al. is regularly used to support the claim that the FPR in fingerprint examination is reasonably low (0.1%).

Studies Show Fingerprint Analysis Is Not 100 Percent Accurate. While people may believe that everyone has a unique fingerprint, this has never been proven, and statistical analyses have not been able to determine the probability that multiple people may have the same fingerprints.

How rare is a false positive test? ›

Public Health England reports that RT-PCR assays show a specificity of over 95%, meaning that up to 5% of cases are false positives. The impact of false positive results includes risk of overestimating the COVID-19 incidence, the demand on track and trace, and the extent of asymptomatic infection.

Following the liberal criterion of Bradley (1978), percentages of false positives between 2.5 and 7.5% were considered acceptable (and shaded). Following a similar logic, the percentages of false negatives under 25% were considered correct (and shaded).

FMR – False Match Rate: An empirical estimate of the probability (percentage of times) at which the system incorrectly accepts that a biometric sample belongs to the claim identity when the sample actually belongs to a different subject (impostor). This metric is an algorithmic level verification error.

Biometric systems can make two basic errors. A “false positive” occurs when the system incorrectly matches an input to a non-matching template, while in a “false negative”, the system fails to detect a match between an input and a matching template.

As noted above, fingerprint scans are accurate at least 98% of the time at worst, with ideal outcomes topping out around 99.91% accuracy. However, biometrics overall do not meet NIST's standards for accuracy. NIST's ideal miss rate is 0.00001% or one error in every 100,000 scans.

Some methods in use on a daily basis involve magnetic and fluorescent powders, alternate light sources, superglue processing, dye stain techniques, and computerized digital imaging. The goal is to detect and capture a faint and almost nonexistent latent trace of a fingerprint.

A false positive is when a scientist determines something is true when it is actually false (also called a type I error). A false positive is a “false alarm.” A false negative is saying something is false when it is actually true (also called a type II error).

Biometric systems can make two basic errors. A “false positive” occurs when the system incorrectly matches an input to a non-matching template, while in a “false negative”, the system fails to detect a match between an input and a matching template.

Specifically, the result of any screening test might wind up being a false negative, indicating that the patient does not have the disease when they do, or a false positive, indicating that the patient has the disease when they do not.

In the context of identity verification, a false positive refers to a situation in which an individual passes the identity check even though they're not who they say they are.