In the world of biometric authentication, two metrics stand out: the False Acceptance Rate (FAR) and the False Rejection Rate (FRR). These metrics measure the effectiveness of a biometric system in verifying the identity of an individual. In this article, we will discuss these two metrics in detail and their importance in the world of biometrics. 

What is False Acceptance Rate (FAR)?

The false acceptance rate measures the probability that a biometric system will incorrectly accept a person's identity. In simpler terms, the FAR is the rate at which the system allows access to someone who is not authorized to access it. The FAR is typically expressed as a percentage and is calculated by dividing the number of false acceptances by the total number of verification attempts.

A high false acceptance rate can have serious consequences, especially in security-sensitive areas such as banks, airports, or government facilities. In such places, a high FAR can lead to unauthorized access to sensitive information, theft, or even terrorist attacks. Therefore, a low false acceptance rate is crucial for any biometric system to be effective and secure.

What is False Rejection Rate (FRR)?

The false rejection rate measures the probability that a biometric system will incorrectly reject a person's identity. In other words, the FRR is the rate at which the system denies access to someone who is authorized to access it. The FRR is also expressed as a percentage and is calculated by dividing the number of false rejections by the total number of identification attempts.

A high false rejection rate in biometrics can be frustrating for users, as they will have difficulty accessing the system even though they are authorized to do so. Moreover, a high FRR can increase the workload of security personnel, as they will have to manually verify the identity of each rejected individual. Therefore, a low false rejection rate in biometrics is also crucial from the user experience perspective since it has a high impact on user retention.

Trade-off between FAR and FRR

This is the crucial bit if you want to understand any discussion on biometric systems: there is a trade-off between FAR and FRR, meaning that reducing one metric will increase the other. For example, if a system becomes stricter in verifying identities, it will reduce the FAR (fewer unauthorized accesses) but increase the FRR (more authorized users rejected). Conversely, if a system becomes more permissive in verifying identities, it may reduce the FRR but increase the FAR.

Offering an optimal balance between FAR and FRR in face authentication is the value of any biometric system. They are not independent. Claiming a false acceptance of 1 in a million means nothing unless the provider also shares the false rejection. A (useless) biometric system rejecting everyone, will have 0% FAR (great) but will have 100% FRR (awful). Similarly, false rejection on its own does not mean anything. A (useless) biometric system accepting everyone will have 0% FRR (great) and 100% FAR (awful).

One single parameter of a biometric system usually defines both metrics. The optimal balance will depend on the specific use case and the level of security required. For example, a banking application may require a lower FAR than an office access control system. Reciprocally, users may more easily accept having to repeat a biometric check when transferring large amounts of money than every single day as they are running for a meeting in their office.

Finding the right balance for your industry

The ideal FAR vs. FRR trade-off depends on the industry’s unique security and usability needs. While lowering false acceptance rates enhances security by preventing unauthorized access, it often comes at the cost of higher false rejection rate, leading to frustrating user experiences. Conversely, reducing false rejection rates in biometrics for a smoother experience can increase the risk of false acceptances, potentially compromising security.

This trade-off plays out differently depending on the industry, as some sectors prioritize strict security, while others focus on seamless user experience.

Banking: Preventing fraud vs. ensuring smooth transactions

Biometric authentication is increasingly used in mobile banking, ATM withdrawals, and high-value transactions. A low FAR is crucial to prevent unauthorized access and fraudulent transactions. However, if the FRR is too high, legitimate users may get locked out of their accounts or face repeated authentication failures when trying to authorize payments.

Gaming: Biometric access control and anti-cheat measures

In online gaming, biometrics are used for identity verification, age restrictions, and fraud prevention. A strict system that minimizes FAR ensures that only authorized players access accounts and that cheaters using multiple identities are blocked. However, a high false rejection rate can cause genuine players to be locked out of their accounts, disrupting gameplay and leading to frustration.

Hospitality: Frictionless guest experiences vs. security concerns

Luxury hotels and resorts are adopting biometric check-ins, room access, and payment systems to create a seamless guest experience. However, if FRR is too high, guests may struggle to access their rooms or complete a contactless check-in, requiring staff intervention and diminishing the convenience factor.

On the other hand, a low FRR without proper security measures could allow unauthorized individuals to enter guest rooms or access premium services fraudulently. 

Mobility: Biometric authentication for vehicle access and ride-sharing

Biometrics are increasingly used in car-sharing services, ride-hailing apps, and autonomous vehicle access to enhance security and personalization. A high false rejection rate in biometrics can leave drivers or passengers locked out of their vehicles, creating service disruptions. Meanwhile, a high false acceptance rate could lead to unauthorized individuals accessing rental cars or ride-sharing accounts.

How to reduce false rejection rate in biometrics

Reducing FRR without compromising security requires a combination of technology, user education, and system optimization.The best biometric solutions offer a well-balanced approach that optimizes security (low FAR) without causing excessive false rejections. Here are best practices to lower FRR while maintaining an effective balance with FAR:

1. Improve sensor quality and biometric data capture

Low-quality cameras often struggle to capture biometric data with precision, increasing the likelihood of false rejections. Upgrading to high-resolution cameras can significantly reduce FRR by improving detection in challenging conditions, such as low lighting or varying environmental factors. However, if the biometric solution is not designed to adapt to these conditions, even advanced hardware may fail to deliver consistent accuracy.

2. Optimize threshold settings for better FAR-FRR balance

Every biometric system operates on a matching threshold—how closely a new scan must match the stored template to be accepted.

• A strict threshold (low FAR) = More false rejections (higher FRR)
• A loose threshold (low FRR) = More false acceptances (higher FAR)

The best biometric solutions adjust this dynamically based on the use case. Security-sensitive environments (e.g., banking and gaming ID verification) require a stricter threshold, while other industries can allow slight variations to reduce FRR and improve user experience.

3. Use AI-driven liveness detection 

Advanced biometric systems use AI-powered liveness detection to distinguish genuine users from false rejections caused by aging, lighting conditions, or minor appearance changes.

Conclusion

FAR and FRR are two important metrics in biometric authentication. A low FAR is essential for security-sensitive areas to prevent unauthorized access, while a low FRR is important for user satisfaction and reducing the workload of security personnel. Finding the optimal balance between FAR and FRR is crucial for any biometric system to be effective and will depend on the specific use case.

Want to reduce false rejection rates in biometrics without increasing security risks? Learn how our advanced biometric solutions optimize both FAR and FRR for seamless authentication.