What is the False Positive Ratio (FPR) and Why Does it Matter?

Facial Recognition Technology (FRT) is a crucial aspect in identity verification across Fintech and Regtech, along the sectors like surveillance. Facial recognition and other biometric authentication methods are evaluated using key performance metrics. That metric is known as the False Positive Ratio (FPR).

Failing to control the false positive rate (FPR) with the help of balanced dataset training, threshold adjustment, and NIST-compliant accuracy checks, it may lead to false matching. Such errors prevent correct identification, impairs the operations and subjects organizations to violating the compliance risks.

False positive (FP) results when there is a wrong declaration of a non-match by a system as a match. An example can be an innocent citizen being mistaken as a criminal as a watch list. False positive rate measures the rate of these wrongly identified positives, or how frequently a system mis-recognizes one of the input images as another person entered in the database.

The false positive rate (FPR) is a specific metric, which is calculated as:

FPR = False Positives / (False Positives + True Negatives)

This rate is relevant to the number of times the system miss-defines a person as a match when he/she is not in the data. Low false positive rate (FPR) is preferable on the systems that are more accurate particularly in high security areas where such systems are needed.

How is Measuring Accuracy Classified? 

Consequences are illustrated in a confusion matrix, e.g. the number of true positives and false negatives. It is necessary in measuring accuracy. When there is two label binary classifier, eg “Normal” and “Abnormal”, the confusion matrix can be configured as follows:

In binary prediction or classification, there are four possible outcomes for any given event:

• True Positive: A True Positive is when we correctly identify unusual data as unusual. For example, we label data as “abnormal” when it truly is abnormal.
• True Negative: A True Negative means correctly identifying data as normal when it is indeed normal. This classification shows that the data is not anomalous.
• False Positive: A false positive happens when normal data is wrongly labeled as abnormal.
• False Negative: A False Negative occurs when data is wrongly identified as usual. This means it is incorrectly classified as not being an anomaly, despite being abnormal.

False Positive Ratio Example in Facial Recognition

Real-World Scenario:

Imagine a government uses facial recognition to identify individuals on a national watch list. Out of 10,000 people scanned:

• 100 are actual matches (true positives)
• 50 people are flagged as matches but are not on the list (false positives)
• 9,850 are correctly not flagged (true negatives)

The false positive ratio example here would be:

False Positive Ratio = 50 / (50 + 9,850) = 0.005 or 0.5%

Even a 0.5% error could result in 50 innocent people being incorrectly flagged, underscoring the importance of carefully tuning the system to minimize such errors.

False Positive Ratio in Fraud Detection

Facial recognition is increasingly used in the detection of fraud, particularly during Know Your Customer (KYC) and onboarding systems. In fraud cases, there is a ratio called false positive in fraud detection, which evaluates how often legitimate users would be labeled as fraudsters despite being genuine.

False Positive Ratio for Watch Lists and Border Control

In applications like border security or law enforcement, the false positive ratio for watch lists becomes critical. Individuals wrongly matched to criminal databases face unjust scrutiny. 

To reduce these risks, facial recognition companies should test their systems using balanced datasets. They should also use multiple methods of biometric verification whenever possible.

What are the KYC False Positive Reduction Strategies

KYC systems must strike a balance between fraud prevention and a seamless user experience. These approaches reduce the number of genuine users being wrongly rejected due to system errors. Key methods for KYC false positive reduction include:

• Facial recognition models are more accurate when relying on bigger and broader datasets.
• Liveness detection helps prevent fraud by checking if the image is of a real person instead of a photo or video. 
• Multi-factor authentication combines biometrics, like fingerprints or facial recognition, with passwords or one-time codes for added security. 
• False positives can be reduced by adjusting confidence levels, although this will have a small negative effect on false negatives.
• Using context-specific scoring thresholds depending on risk.

Why False Positive Ratio Matters

• A false positive ratio reflects the rate of incorrect identity matches.
• A high false positive ratio in fraud detection may cause customer churn.
• False positives can cause privacy law violations as well as  financial and reputational damage. 
• It is optimal to use targeted strategies to reduce false positives helps build trust and ensure compliance.
• In KYC workflows, tuning systems for lower FPRs improves conversion and reduces manual reviews.