Today’s landscape is dominated by digital transactions and interactions. This has increasingly put digital identity verification on the hot boiler for businesses and government entities alike.

Fueled by advancements in information and technology, increased emphasis on personal and data security, and the need for seamless and efficient identity confirmation processes, several exciting trends are emerging in the field of biometric ID verification. 

Moreover, according to AU10TIX, the primary mission of any identity authentication mechanism should be to obliterate fraud while furthering a more secure and inclusive world. 

Continuous authentication: enhancing security

Biometric authentication, by definition, captures the unique physical traits of the person of interest and verifies it with an authenticated template of user data. In most scenarios, this takes place only once at the initial access attempt.

However, continuous authentication takes it a step further by continuously monitoring and validating the user’s biometric traits throughout their entire session or interaction. 

The system, more often than not, is so refined that the continuous vigilance through identity verification moments does not create a bad experience for the person under the radar. 

This continuous monitoring ensures that only the authorized user can maintain access, even if their session becomes compromised. This enhanced process can be extremely useful in banks and high data-sensitive zones. 

Behavioural biometrics: adding a new dimension 

Behavioral biometrics is a field of identity verification that is based on distinct patterns in user behavior like typing rhythm, touchscreen gestures, or voice recognition. This is innately different from the conventional biometric procedure as unique physical parameters, like retina and face patterns, do not play a defining role here. 

Profiling a person based on distinctive behavioral patterns facilitates continuous verification and authentication using real-time behavioral pattern analysis. The advantage of this system of authentication lies in its ability to adapt and recognize changes in user behavior. This makes it near-impossible for unauthorised individuals to replicate or imitate identity.

Moreover, behavioral biometrics are also getting traction in diagnosing and assessing mental health markers. Parameters like heart rate and instant spikes in glucose levels can be indicators of anxiety or some underlying mental health problem. 

Biometric fusion: robust authentication

Biometric fusion is a technique that adds a very cohesive layer to conventional identity verification by combining multiple biometric modalities. Instead of relying on a single biometric trait, like fingerprints or facial recognition, this process integrates additional biometric parameters. 

There are varying levels at which this fusion can take place. It can primarily occur at the sensor and feature levels. The sensor level is a dynamic authentication platform, whereas the feature level is a static one. 

At the sensor level, multiple sensors capture different biometric data in real time and then aggregate and analyze them for verification. The feature level involves the accumulation of other biometric traits and then merging them later, to create a more comprehensive and unique identifier.

By combining various biometric factors, the system achieves robust and dependable authentication, making it much more difficult for malicious individuals to deceive or trick the system. This method minimizes the risk of false positives or false negatives.

Post-quantum biometrics: future-proofing security

Biometrics, such as fingerprint scanning, facial recognition, and behavioural authentication, have become an integral part of our daily lives. Yes, these mechanisms are far user-centric as they do away with remembering alpha-numeric passcodes, but they are not immune to breaches. 

They involve the transmission of data from one place to another (such as a fingerprint reader sending a user’s fingerprint to a cloud server for verification)

Although encrypted when sent, it is still possible for it to be intercepted, either at the fingerprint reader or cloud server, in case they are compromised.

Quantum computers can break the cryptography used in conventional passwords(even biometric ones) that are generally considered safe in regard to contemporary computers. This means that data not secured by the newly developed quantum-resistant algorithms would be vulnerable.  

Lattice-based cryptography is an emerging technique in post-quantum biometrics. Mathematical structures known as lattices serve as the base on which encryption schemes that resist quantum computer attacks rest. 

Researchers are actively exploring lattice-based algorithms like NTRU and Learning With Errors (LWE) for their potential application in post-quantum biometric systems.

Code-based cryptography is another category of post-quantum algorithms that relies on error-correcting codes to enhance security against quantum attacks. Code-based algorithms such as McEliece and Niederreiter are notable examples within this category. These techniques aim to ensure resistance against the ill effects of quantum computing capabilities.

Takeaway

Biometric identification was a revelation when it first surfaced. With the exponential advancements in technology and data analysis, a number of exciting and mind-boggling trends have come into existence and the process is not slowing down anytime soon.

These fantastic technologies seem impenetrable. But, as the safety industry advances, so will the mindset and tools of hackers. Nothing can be a foolproof solution for eternity. Embrace the changes till they are helpful.

Tim Williamson, a psychology graduate from the University of Hertfordshire, has a keen interest in the fields of mental health, wellness, and lifestyle.