Blog 25 Apr 2024
How AI Facial Recognition Fortifies Identity Proofing

How AI Facial Recognition Fortifies Identity Proofing?

Author: teresa_myers | 25 Apr 2024

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

Image AcquisitionMultiple frames of face images are captured using imaging devices.

  • Video cameras, Smartphones with high-resolution cameras, webcams, and high-tech CCTV cameras are used to capture these images.
PreprocessingAfter image acquisition is completed, preprocessing steps are applied to refine the quality and readability of acquired images. 

It includes:

  • Face Detection: Done through biometric readers by locating and isolating the face images from complete capture through AI.
  • Normalization: The image is then adjusted to the required size, orientation, and lighting conditions required for recognition.
  • Alignment: The face image is centrally aligned according to the capture zone.
Facial Feature ExtractionThe distinct facial features are extracted from the preprocessed acquired face images. It uniquely represents the human face image correctly identifying all the features. Normally, a facial recognition system extracts three types of facial features:

  1. Geometric Features: Complete face geometry including eyes, nose, lips, etc.
  2. Texture Features: Skin, wrinkles, facial hair, etc,
  3. Statistical Features: facial regions according to the statistical probabilities for face matching.
teps-involved-in-ai-facial-recognition
Facial Feature RepresentationThe extracted facial features are algorithmically and aesthetically combined with sophisticated software to create:

  • A back-end mathematical representation of a face
  • A feature-vector representation of a face
Face Database Enrollment & Face MatchingThe two feature representations are further worked upon for database matching. The following algorithms execute this step:

  • Euclidean Distance is measured between the feature vectors using the formula:
  • Cosine Similarity is determined for feature vectors using the following formula:
    Euclidean Distance Formula

            Cosine Similarity (A, B) = A . B / ||A||B||

  • Machine Learning Classifier use algorithms to classify and recognize faces from feature vectors and images
Decision MakingThe AI face-matching solution makes highly accurate decisions as to whether a face has matched an Identity present in the given database or not.

  • Here, the threshold of False Match Rate (FMR) and False Non-Match Rate (FNMR) is set according to the required stringency in face ID checks.
  • In case a face match is found, the feature vector associated with the digital facial identity is returned to the system for verification decisions.
OutputThe final output image with a face match score based on the system’s AI-based confidence score is given for the Identity verification decision.

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:

FNMR CategoryTesting Algorithm for Image TypesIdeal False Non-Match Rate
Constrained, Cooperative
  • VISA, VISA Border, VISA > 45 yrs.
  • Mugshot, Mugshot > 12 yrs.
For VISA and VISA Border = 0.000001

For Others = 0.00001

UnConstrained, Non-Cooperative
  • Border, Kiosk
0.00001

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

According to Gitnux, 71% of retail businesses find facial recognition as a useful security measure.
iPhone X showed a 0.5% false reject rate in its facial ID feature. This is by far the most accurate facial recognition done through a smartphone. It is because of AI facial recognition technology that Apple uses is uniquely programmed to produce highly accurate results.
China currently leads the facial recognition technology market having 200 million surveillance cameras having face recognition capabilities.

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

Watch to learn more:

Try Facia and experience the fastest proactive digital identity protection.

Frequently Asked Questions

What is AI facial recognition?

AI facial recognition is a technology that uses AI facial recognition to identify or verify individuals based on their facial features. It analyzes digital images or video frames to match faces against a database for identification.

How does AI face recognition work?

It begins with face detection, which identifies faces within an image or video. Then, key features like eyes, nose, and mouth are extracted. The system maps these features into a digital template using mathematical algorithms. This template is then compared with a database to find a match.

What type of AI is used for facial recognition?

Machine learning models, especially deep learning neural networks, are widely used for facial recognition. Convolutional Neural Networks (CNNs) are particularly effective due to their ability to analyze visual data and recognize complex patterns.

How to use AI for facial recognition?

Facial recognition has various applications, including:

  • Security and Law Enforcement: Identifying suspects or verifying identities at airports and borders.
  • Social Media: Tagging people in photos and suggesting connections based on facial recognition.
  • Access Control: Unlocking devices or securing buildings through facial verification.
  • Retail: Identifying repeat customers or offering personalized recommendations.
What are the problems with AI facial recognition?

While beneficial, AI facial recognition faces some challenges:

  • Accuracy Concerns: Facial variations, lighting conditions, and database biases can affect accuracy.
  • Privacy Issues: Concerns exist about mass surveillance and potential misuse of facial recognition data.
  • Bias and Discrimination: AI algorithms can inherit biases from training data, leading to misidentification.
  • Regulation and Ethics: Clear regulations and ethical frameworks are needed for responsible use.