The Impact of Biometric Recognition Failures on Security

Systems for access control, identification, and authentication all make heavy use of biometric recognition methods. Because it uses distinctive biological characteristics of individuals, such as fingerprints, facial features, and iris scans, to give access or confirm identity, biometric recognition is regarded as a secure authentication method.

Like any technology, biometric recognition techniques are not 100% reliable and occasionally fall short. The integrity of the system and the data it safeguards may be jeopardized by these failures, which can also result in major security risks.

In this post, we'll talk about how security issues are affected when biometric identification systems go wrong, using examples from actual situations.

Types of Biometric Recognition Failures

Biometric recognition failures can be broadly categorized into two types: false acceptance and false rejection.

False acceptance

When someone is mistakenly identified by the system as an authorized user when they are not, it happens. This failure may arise for several reasons, such as bad biometric data or insufficient security mechanisms in the system. Unauthorized access to secure locations, information, and systems may result from a false acceptance failure.

False Rejection

This problem often arises when the system fails to identify a legitimate user and refuses them access. This kind of failure can happen for several reasons, including bad biometric data, changes in an individual's biometric data over time, or programming faults in the system. False rejection failures may prevent authorized users from accessing secure areas or systems, which may have a detrimental impact on productivity.

Failures in Biometric Recognition Examples

Let's examine a few instances of biometric recognition failures and how security concerns are impacted:

False Acceptance in Facial Recognition Technology

The American Civil Liberties Union (ACLU) tested Amazon's facial recognition software, Rekognition, in 2019 by comparing images of members of Congress to a database of 25,000 publicly available arrest photos. The findings revealed that 28 members of Congress were mistakenly listed by the system as having been detained for criminal activity. This incident showed that facial recognition technology could have high false positive rates, which could result in innocent people being mistakenly classified as criminals.

False Rejection in Facial Recognition Systems

The use of facial recognition technology in security, surveillance, and authentication systems has grown in popularity in recent years. Yet, a variety of factors can sometimes cause technology to malfunction. For example, a news report from 2020 said that the facial recognition system employed by the Detroit Police Department had a high false rejection rate, resulting in innocent people being mistakenly labeled as suspects. This failure emphasizes how crucial it is to verify facial recognition algorithms for accuracy and make sure they are not prejudiced against particular groups.

False Acceptance in Voice Recognition Systems

Numerous more applications, such as virtual assistants and banking systems, use voice recognition technologies. Technology, however, is not perfect and occasionally fails for a variety of reasons. For example, a news piece from 2017 detailed how a UK bank was forced to halt its voice recognition technology after it was discovered that the system could be tricked by a fraudster using a vocal recording of the account holder. The system featured a high percentage of fraudulent acceptances, which allowed someone else to access the account holder's money without authorization.

False Rejection in Fingerprint Recognition Systems

One of the most popular biometric verification techniques used in a variety of applications is fingerprint recognition. Nevertheless, the technology is not infallible and occasionally fails for several different reasons. For example, filthy or dry hands can degrade the fingerprint's quality and cause it to be mistakenly rejected. A Malaysian study in 2013 found why most people experience trouble using fingerprint recognition-based biometric technology. Due to dry skin, many people's fingerprints get easily rejected by the recognition system, which may lead to identity verification failure.

False Acceptance in Signature Verification Systems

Systems that use a person's signature to verify their identification are called signature verification systems. This technology is not impervious, though, and it occasionally fails for several different reasons. For example, a news story from 2018 detailed how a bank in India was required to compensate a customer whose account was compromised as a result of a false acceptance error in the bank's signature verification system. The system had authorized a fake signature, which allowed someone else to get access to the customer's account.

False Rejection in Voice Recognition Systems

Virtual assistants and authentication systems are only two examples of the many uses of voice recognition technology. Technology, however, occasionally fails for many reasons. A UK Bank's voice recognition system, for example, had a significant false rejection rate in 2020, according to a news item, which resulted in authorized users losing access to their accounts. The reason for the failure was said to be a change in the customers' voices brought on by things like background noise and changes in their health.

False Acceptance in Iris Recognition Systems

Iris identification technology verifies a person's identity by using distinctive patterns in their iris. Nevertheless, this technology is not infallible and occasionally fails for many different reasons same as we saw in the facial recognition failure section. For example, in 2018, researchers from the University of Barcelona showed how to mislead iris recognition systems by making fake iris images that may fool the system into mistaking one individual for someone else. This failure demonstrated the possibility of spoofing attacks against iris recognition systems.

Conclusion

Failures in biometric recognition can have significant security repercussions while verifying users, resulting in unauthorized access, data breaches, and other unfavorable outcomes. It is crucial to put strong security measures in place, test biometric identification systems often for precision and dependability, and deal with any biases or vulnerabilities that might exist in the technology. By doing this, we may contribute to ensuring that biometric identification stays a safe and dependable authentication technique.