17 Apr 20247 min readin ID verification & biometrics

How to Employ Face Recognition Process in Identity Verification and Why

Andrey Terekhin

Head of Product, Regula

At times, identity verification resembles the wide variety of Lego bricks. There are basic bricks, without which you can’t build castle walls. Missing one brick results in the risk of the whole construction collapsing. There are also fancy bricks, such as flags, or even a dragon to sit on the wall. You can go without them—but it’s definitely less fun.

In this analogy, face recognition isn’t a basic brick for just any identity verification workflow—save for surveillance, perhaps. However, it definitely adds an additional layer of security for most businesses.

In this post, we’ll discuss how a face recognition process works in identity verification, and cover three scenarios in which it’s 100% worth a shot.

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What is face recognition?

Perhaps you've boarded a flight using just your biometric data, entered a secured area after a camera identified your face, or even paid for groceries simply by looking into a camera. These scenarios highlight the increasing use of face recognition in everyday situations.

Face recognition is a biometric technology that involves identifying or verifying an individual based on their unique facial features. The principle is simple: once you have an image of an individual, the technology searches through a database of faces to find whether there’s a match. In our example with payments, the bank system literally looks through all their clients' profiles to identify the one who’s in front of the cashier at the moment. 

This search is performed on a one-to-many basis (1:N): one face is compared against numerous other faces, with N referring to the size of the database.

 face recognition principle, also known as face matching

Typically, face recognition yields a similarity rate rather than a binary match or no-match result. This is because faces are subject to variations such as changes in expression, capture angle, makeup, accessories, and age, among other factors.

Is face recognition the same as face matching?

As for face recognition and face matching, they are different procedures from a business standpoint, even though one is based on the other.

Face matching here refers to face verification that is performed on a 1:1 basis. It’s when you have an ID document and a selfie of a person, and your goal is to confirm that it is the same person in the two photos. 

On the other hand, face recognition involves identifying a specific individual within a database using a single photo. If you need an alternative term for face recognition, then it’d be “face search” or “face identification.”

💡The field of biometrics is rich in technologies, techniques, and corresponding terms. We’ve even published an explainer to help you understand the differences between the most widespread items.

What is a face recognition algorithm like in identity verification?

A basic face recognition flow includes three major steps: detection, analysis, and comparison. Ideally, there should be one more: image quality assessment. 

Let’s have a look at each.

  1. Detection: The system identifies the presence of a face in an image or a video using algorithms that can detect facial features—also known as landmarks—such as eyes, nose, and lip corners. Once the face is detected, the algorithm fetches an image of it for further processing.

  2. Quality assessment: Advanced identity verification solutions recommend implementing this step to enhance the reliability of face recognition. Image assessment ensures the image meets specified quality standards, and there are no occlusions like face masks or glasses. Still, this step may be omitted if the image quality is assured, such as when photos are taken by staff in a controlled environment rather than being submitted by users online.

  3. Analysis: By identifying unique characteristics of the face, the software creates a distinct facial signature, or descriptor, for each individual. These descriptors are like points in space. For images of the same person, they are close, and for images of different people, they are far away. The closer the descriptors are, the higher the similarity.

  4. Comparison: The obtained descriptor is then compared against a database of known faces. Based on the comparison results, the software determines whether the provided facial image has a significant similarity rate with any existing entry in the database. Depending on the thresholds set within the face recognition solution, the obtained value can be interpreted as a match or no match.

face recognition algorithm

Why face recognition?

As we wrote in another post, integrating a facial recognition component into your identity verification workflow is a great way to safeguard against unauthorized access to your systems, platforms, and even brick-and-mortar facilities.  

After a customer confirms their identity, their facial biometric data gets included in a digital profile stored in the system. Then, when they need to verify their identity again, the system checks any new selfie they provide against this profile. If the new photo matches well enough, the system knows it's the same person, and grants them access.

But why opt for facial recognition over other authentication methods?

Given the prevalent risks associated with phishing, poor password hygiene, and social engineering practices, more companies are turning to a passwordless approach. Biometrics, particularly facial recognition, emerges as a promising solution for ubiquitous internet sign-ins.

4 examples of scenarios where face recognition is a game-changer

While some conventional applications of face recognition technologies may smack a bit of Big Brother, there are plenty of recent use cases where it can work for a good cause. Here are four use cases where this technology makes a difference, either in security or in user experience:

→ Password recovery process. The vulnerability of knowledge-based authenticators, such as passwords, secret phrases, or even PINs, to theft by scammers is well-documented. With face recognition technology, individuals can regain access to their accounts, preserving both convenience and security, by simply capturing a new selfie. 

→ Entry/exit process during mass events. Managing the influx of attendees during large-scale events often creates various risks, from a tussle to get in first to presenting fake passes. When face recognition is turned on, attendees can simply look into the camera for identification, minimizing congestion at entrances and exits. This elevates the experience and also helps organizers accurately track attendance.

→ Security in the sharing economy. Whether it's ride-sharing platforms or accommodation rentals, integrating facial recognition enhances authentication processes. The technology largely contributes to mitigating fraudulent activities and fostering a more trustworthy environment for both service providers and consumers.

→ Fighting gambling addiction. With face recognition technology, individuals can voluntarily enroll in self-exclusion lists, effectively prohibiting their entry into casinos and betting venues. By leveraging facial biometrics, these programs provide guardrails that empower individuals to take control of their addiction and seek support for recovery.

How to implement face recognition in your workflows

Building a reliable face recognition system requires significant effort. At a minimum, you must:

  • Develop a face detection algorithm that reliably identifies faces.

  • Train descriptor comparison algorithms to ensure accurate results when comparing two images.

  • Create a scalable system capable of searching for unique faces among a vast number of images.

While it's possible to undertake this work in-house, it demands considerable time, and might not align with your company’s core expertise.

Regula has been honing its competence in identity verification for more than 30 years. By integrating our solutions, which feature a pre-configured face recognition component, you can achieve the quickest time-to-market without sacrificing reliability.

Regula Face SDK enables biometric verification with rapid and accurate face recognition, liveness detection, and face matching and identification capabilities, which are functional across any user device. This ensures that only live, authenticated individuals get access to your services, providing you with unparalleled security and peace of mind.

Regula Face SDK

Make face verification fast and secure

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