A false rejection is an error in a biometric system that doesn’t grant access to a legitimate person. A false rejection rate (FRR) is a percentage of how many times the biometric system rejects an authorized person.
In addition, continuous rejection can annoy users. Too many false dismissals signify that the system is not able to recognize authorized people efficiently. Frequent rejection will also waste the time of the employees or users, and these rejections will eventually lead to mistrust of the security system.
Factors That Can Affect FRR
Several factors can affect the false desertion rate. Unclear, fingerprints and low-resolution pictures can result in a false rejection. A biometric database contains a large database of different people of different ethnicities which helps the system learn to identify a larger number of users. If there is limited or specific data given to the system it will result in a false dismissal for the other groups. Besides, facial expressions and aging can also reject legitimate identity.
Moreover, if the system is provided with the authentic data of a person it will never lead to the rejection of a legitimate user. The database should contain real-world data of users to make the system work accurately. On top of that, false desertion can also occur due to improper lighting. It will not allow the system to capture accurate facial features.
Furthermore, a crowded place can also lead to a false dismissal because the system will not be able to identify the particular identity. Also, the face should be present in front of a camera. The system will not be able to provide authorization if the camera is placed in an awkward place.
How to Minimize 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-Trained Users
Users should know the best use of the biometric 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.
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 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.