Introduction

To make sure that only real people are allowed access to services and systems, face match, used in facial recognition technology, matches the face of a claimed person with the available dataset. The effectiveness of face-match technology has significantly increased due to recent developments in AI algorithms and machine learning techniques, guaranteeing precise ID verification. 

Online face matching is used by many industries throughout the world, such as law enforcement, retail, e-commerce, financial institutions, and ID verification, in their authentication systems to guarantee exact and accurate person verification. The technology’s great accuracy and increased efficiency have attracted a lot of interest. It greatly improves the protection of sensitive data from exploitation; for example, attempts to obtain unauthorized access to systems by using someone else’s identity are quickly identified and stopped.  

How Does the Technology Work?

Face matching online has become more accurate and efficient because of advanced machine learning, specifically convolutional neural networks (CNNs), 3D facial recognition, and computer vision. Here is a brief explanation in steps about how face-matching technology verifies people.  

1. The multi-faceted face-matching process begins with capturing the image or live video of the claimed identity. High-resolution cameras are used to capture high-contrast and clear images, for accurate recognition. 
2. Face detection refers to separating the face from the image or live videos. For instance, to make it simple, it’s like detecting a face from a frame, and isolating a face from other objects like a background to focus on the main target.  
3. Once the face from the image or live video is detected, the next process involves analysis of facial features the distance between the eyes, depth of the mouth, shape of the nose, and contour of the jawline. After analyzing the facial attributes, the acquired features are extracted to generate a facial template, a mathematical expression of distinctive face key points. 
4. Face comparison refers to comparing the facial template, which serves as the digital map of the face, against the available dataset of identities to verify the authenticity of the identities. 
5. This is the last and crucial stage in AI face match based on which access is granted to systems. If the claimed face exactly matches the already existing identity, access is allowed, if the face shows no match, the identity is robustly restricted from access to the services & networks. 

Two Common Approaches of Face-Matching Technology

To provide appropriate results, face matching involves comparing the facial template with the existing collection of identities. It serves as the foundation for facial recognition software, which makes ID verification technology safe and dependable. Two popular methods for precise and trustworthy authentication are face comparison: 1:1 and 1:N face matching.  

• 1:1 Face Match

This method, which is also known as verification or authentication, entails confirming that the claimed identity is that of the same person by comparing it to their known dataset. Systems allow access to the individual if the claimed identification matches the reference dataset; if not, the claim is rejected right away. For example, when someone tries to unlock a mobile phone by scanning their face, if they are the correct person, the phone unlocks instantaneously. However, if someone is using a fake identity, facial recognition software is unable to identify them and denies them access to the phone. By verifying real people and preventing fraudulent identities, a 1:1 facial match reduces FAR and FRR rates.  

• 1:N Face Match

The 1:N technique, also known as identification or recognition, verifies that a person does not belong to a high-risk individual by comparing their stated identity to all accessible datasets, such as watchlists, criminal databases, sanctions lists, or PEP lists. Because it helps identify high-risk clients, screen for adverse and thorough due diligence, and reduce the possibility of financial crimes, this is also an essential component of anti-money laundering compliance.  

Conclusion

The use of technology raises privacy and ethical issues such as widespread surveillance, processing of biometric data without express consent, and biased authentication. However, cautious technological use, such as obtaining individuals’ express consent before processing their data and testing algorithms on a variety of datasets, can yield promising results in several global fields.