How AI Facial Recognition Fortifies Identity Proofing?

We witness today what we fantasized about decades ago. Yes! AI is here and its pervasive dominance and influence across multiple industries is inevitable. AI is reshaping digital identities strikingly and international bodies like NIST are continuously developing new algorithms in response to its ever-evolving capabilities in detecting identity fraud attempts.

AI Facial Recognition is expected to grow to US $7 billion this year whereas the global facial recognition systems market is said to grow to US $9.9 billion. By 2025, 90% of new cars will have facial recognition systems embedded for security and safety purposes. (Gitnux)

In this blog, we will explore the technical relationship between AI Facial Recognition and Identity Verification and how these two become an unparalleled proactive system for protecting your identities digitally.

After Reading This, You Will Understand That…

• AI Facial Recognition works in an automated way using algorithms and deep learning.
• It is a 7-step comprehensive process that creates a digital facial image that can be used as a verified digital identity.
• NIST sets the standards of an ideal facial recognition solution.
• Verification speed impacts the efficiency of a liveness solution in guarding digital identities against spoofing attempts.
• Facia has a state-of-the-art solution testing lab leaving no stone unturned to achieve perfection in identity verification.

Brief on AI Facial Recognition

Artificial Intelligence has been successfully integrated with facial recognition through algorithms and deep learning. AI Face recognition detects and face match patterns that are captured in video or images through high-tech cameras and facial recognition solutions. AI facial recognition involves complex background technical work in various steps. Let us take a look at the working of an AI-based Facial recognition solution.

Steps Involved in AI Facial Recognition

How AI Facial Recognition Technology’s Accuracy is Measured?

Measuring the accuracy of a Facial recognition solution depends on multiple notions. However, the most effective way to measure the accuracy is to follow the NIST guidelines mentioned in its regularly updated report entitled FRTE (Facial Recognition Technology Evaluation). Recently, the document was republished with updated insights aimed to serve as a benchmark for automated facial recognition technologies. 

FRTE evaluated different facial recognition solution providers under four categories of facial image types:

How Verification Speed Impacts AI Facial Recognition?

Reaching the benchmark is no less than a challenge especially when it requires meeting the speed goal in verifying Liveness and other aspects of facial recognition. It is to prevent any spoofing attempt to ensure no time window is given to a threat to bypass Identity processing. Speed in verification is aimed at two major goals:

• Preventing identity spoofing threat vectors in less time
• Ensuring seamless user journey.

To achieve this IDV solution testing is carried out in which multiple spoofing attempts are carried out on the solution internally. This helps in improving an IDV solution’s speed and accuracy according to the NIST standards in biometric facial recognition as explained above.

Read More: How To Prevent From AI Face Swap Online Attacks

Few Interesting Insights

How Facia Does Its Testing?

Facia is a cutting-edge facial recognition solution that ensures the highest possible standards in identity verification. Facia’s state-of-the-art testing lab empowers the world’s fastest liveness detection solution by testing it under different conditions and spoof attempts.

The insider’s scoop on liveness verification Buyers Guide

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Try Facia and experience the fastest proactive digital identity protection.