April 11, 2021

Emergence of AI and Biometrics In 2021 What’s Coming Ahead?

Undoubtedly, security is becoming a hot topic in different evolving industries. Major data breaches are happening with every passing year. Businesses and financial infrastructures are crawling for innovative ways to beef up their security systems and access control to stay one step ahead of data hackers. 

Side effects of the global COVID-19 pandemic and cybersecurity statistics disclosed an immense growth in breach or hacked data from the most common sources such as mobile and IoT devices. COVID-19 has erected remote workforces, making indoors for cyberattacks. In 2019, 88% of the organizations globally experienced spear-phishing attempts. 

Thanks to AI-powered biometrics and security innovations to which businesses are integrated themselves for enhancing their security infrastructures. User authentication on the basis of physiological characteristics and behavioral biometrics is becoming a new normal due to technological advancements and the way the number of hackers is rising in this era of digitization. 

How AI Biometric Technology Works? 

Individuals are authenticated on the basis of their behavioral and physical characteristics for combat chargebacks and fraudulent activities. There are two major categories of biometric authentication solutions: behavioral and physical. 

  • Behavioral biometric solutions include unique behavioral attributes, such as a person’s interaction with a device, a person’s typing rhythm, voice, gait, etc. 
  • Physical biometric solutions include measurable and distinctive characteristics, such as a person’s facial features, fingerprint, iris, DNA, etc. 

This encoded biometric data of the individuals is stored in a database and then digitally sampled during the process of authentication and verification. 

Voice Recognition 

Voice or speaker recognition differs from speech recognition. Former identifies and recognizes a speaker with the help of voice biometrics. Later, analysis is performed on what is said by the individual. Voice biometrics includes behavioral characteristics such as tone, pitch cadence, etc, and physical characteristics such as the shape of the vocal tract which is responsible for controlling and articulating speech production. In voice biometrics, words are digitized by reducing them to segments that comprise encoded frequencies or formants and produces a model voiceprint unique to every individual. 

Fingerprint Recognition 

Biometric fingerprint solutions analyze certain features of a fingerprint such as the valleys between the ridges, and ridgeline patterns on fingers, which are then converted for digital data storage.  Biometric systems must find an adequate number of minutiae patterns to get a fingerprint match for user authentication and verification. 

Face Recognition

Facial recognition solutions involve extraction and comparison of selected facial features from a digital video frame or image for user authentication and verification. It involves such innovative algorithms that analyze the distance between two eyes, the shape of cheekbones, depth of eye socket, the width of the nose, etc. user’s facial features are extracted, encoded, and is saved in the database as “faceprints” which can be utilized in future to find appropriate matches in the destination database. 

Behavioral Biometrics 

Identification and measurement of human behavior and activities are included in behavioral biometric identification. Behavioral biometrics include device usage, voice print, signature analysis, keystroke dynamics, and error patterns. These behavioral biometrics are effectively utilized as an extra layer of security along with other biometric information or credentials. There might be a possibility when the original user shares his/her credentials with another person after successful authentication. To minimize this possibility, behavioral biometric solutions are incorporated for the analysis of user interaction with devices that vary from normal usage patterns. 

How AI is Smarter Than Biometrics? 

The emergence of machine learning and artificial intelligence has shown their unexpected potential in cybersecurity. No doubt, biometrics are reliable for accurate authentication mechanisms. Rapid technological advancements such as artificial intelligence can outshine biometric systems. 

Do you know that AI-generated synthetic fingerprints can fool biometric systems? Researchers are capable of developing machine learning algorithms to develop synthetic fingerprints. AI-generated fakes can be so accurate and unique that they can easily dodge biometric systems. Such fingerprints can be utilized for brute attacks and can test every generated fingerprint until the target device is hacked. Hence, the combination of artificial intelligence and biometric authentication plays a crucial role to protect devices against cyber attacks and fraudulent activities. 

Stay Ahead of Uncertainty 

By the year 2025, biometric hardware and software will tend to grow $15.1 billion globally with a CAGR of 22.9%. Researchers predict that the cumulative biometric revenue will total $69.8 billion from 2016 to 2025. The largest revenue segments of biometrics will be face verification, voice recognition, fingerprint recognition, and iris recognition. Also, the iris recognition market will evolve to $4.1 billion by the year 2025. 

Thanks to the evolving artificial intelligence and the way it’s enhancing biometric authentication services. AI-powered behavioral biometrics are utilized efficiently, effectively, and accurately to prevent fraudulent activities in financial infrastructures. Behavioral biometrics are capable of providing frictionless, continuous, and passive authentication. More advancements can be seen in the near future as more and more financial infrastructures will synergize with innovative artificial intelligence and machine learning algorithms for enhanced identity verification solutions. Over the next 5 to 8 years, machine learning and behavioral biometrics are set to restructure the authentication landscape.

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