An inaccuracy in a biometric system that prevents a valid person from being granted access is known as a false rejection. The percentage of times an authorized user is rejected by the biometric system is known as the FRR meaning a false rejection rate. Besides, when the system is unable to allow access to a legitimate user then it is known as a false acceptance rate (FAR).
Furthermore, persistent rejection may irritate users. An excessive number of false dismissals indicates that the system is ineffective at identifying authorized users. Employees or consumers will lose time if they are repeatedly rejected, and this will eventually cause them to lose faith in the security system. The biometric systems must be upgraded over time to make the whole process smooth.
Which Factors Can Help in Minimizing False Rejections?
To maintain the trust of the users and a secure environment, the false reject rate should be fixed as soon as possible. It can be reduced by enhancing algorithms, and setup considerations, and its users should be well-trained.
Enhanced Algorithms
The system should use a better way to analyze the data of an individual from shared data. Also, the implementation of machine learning can help the system work more accurately than before. It will automatically learn and improve itself from the mistakes. Besides, the system should be well-balanced when it comes to sensitivity. Too strict regulations can make the system allow unauthorized users and the system with too convenient rules can refuse the legitimate users.
Setup Considerations
During the setting of the system, high-quality sensors should be opted for better performance. The system should also be designed in a way that it can work properly in different conditions such as crowds and other environmental conditions. In addition, the system should be easy to use for the users. Difficult and prolonged procedures often irritate the users.
Well-Aware Users
Users should know the best use of the biometric liveness detection systems. They should be given proper training for a few days about using the device. Also, there must be a feedback machine or it can be taken verbally to enhance or improve the working of the system as per the user’s experiences.
Incorporation of False Rejection Rate in Biometric Systems
There are many organizations that have employed biometric systems in front to verify each customer. FRR is an important factor and concern that can be noticed in a variety of ways and places such as:
Fingerprint Scan
It is one of the most common and widely used systems of verification that allows users to scan their fingerprint pattern to get access. If there is a high rate of false rejections in the system, it means that the system is unable to verify the authorized person and has rejected the existing fingerprint pattern.
Facial Recognition
This system compares an individual’s facial features with an existing database. FRR can affect the system’s efficacy when it continues to reject authorized people. For example, the system might reject an individual wearing accessories like glasses and a hat.
Iris Scan
This verification method allows the users to scan their eyes which contain a specific and unique pattern that can never be copied by any scammer. The system can result in a high false rejection rate when it is unable to verify the user due to any disability or blink.
What Can Be the Possible Developments in Biometric Systems?
- Possible developments can make the proper use of biometric systems. Over time, it can be improved to maintain security levels and user trust.
- If the system is incorporated with new algorithms and improvements, it will work accurately.
- Alongside, more than one sensor also improves the experience of the user’s biometric system. It will work in a way that if one sensor is troubling, the other can verify the user’s data.
- Machine learning is one way to minimize the rejection rate. With this learning system, the system will automatically learn from its mistakes and improve its performance. Also, the institutions can opt for wearable monitoring devices which can allow legitimate users to access the system without being refused.
- The system will likely be designed to verify the user by using different methods. For instance, if a user is being rejected by using a fingerprint, it can use another way like an iris scan or face scan to verify the user. It will effectively improve the rejection rate.
- Continuous advancement can be seen when it comes to AI. Constant research into human behavior and biometric systems can introduce a new method to verify the users and will eventually lead to fewer biometric FRRs.
Conclusion
FRR is a crucial error in the systems that need to be addressed promptly. If it continues, then it can result in the mistrust of the user’s on the verification systems. This negligence can also result in any kind of fraud.
