05 Apr 20247 min readin ID verification & biometrics

Face Recognition vs. Face Verification in Identity Verification: The Expert Explanation

Andrey Terekhin

Head of Product, Regula

Imagine snapping a selfie to open a bank account when suddenly the app asks for a gesture, say, a nod, that confirms, “Yes, it’s really me.” At moments like these, face recognition and face verification, the often mixed-up cousins, reveal their true colors. And if that wasn't enough to confuse you, terms like “face search,” “face identification,” “face detection,” and “face matching” join the mix, each playing its role in identity verification. 

In this article, we delve into what sets these technologies apart, and discover why understanding their differences is more than just a matter of semantics.

Let’s get started.

Face detection

Face detection is the starting point of all face biometrics-related technologies. Its purpose is to identify the presence of any face in an image (or video) without yet delving into the specifics of who that face belongs to.

face detection example

Face detection is the technology behind your camera's ability to highlight faces with squares before taking a photo, ensuring that every person is in focus.

Face recognition

Face recognition technology allows you to sift through a database of faces to find a match on a one-to-many basis (1:N). The search is performed using a biometric template built upon the unique facial features of a particular person. This accelerates the process and allows you not to store the actual photos, which is important for privacy compliance purposes.

Unlike face detection, which spots any face, face recognition looks for particular faces. In this context, face recognition, face identification, and face search are synonyms, and can be used interchangeably.

The technology is extremely useful because it allows you to reuse a whole pool of biometrics data that has already been gathered to enhance security.

Consider the challenge of identifying a fraudster using multiple fake IDs to breach a system. Traditional methods might fail, as the names and numbers on these documents are different. Yet, the fraudster can’t change one crucial detail—their biometrics. Their face remains the same across all IDs. Thanks to face recognition, such attacks can be prevented.

Another example is the case of a new taxi driver. During the onboarding process, the driver’s biometric data is collected and verified. When they sit behind the wheel, the system employs face recognition for authorization. 

Also, airports and other high-security areas are increasingly adopting face recognition. Their systems can quickly run a check to make sure a passenger isn’t on a watch list (or other database they might have) during a check-in or border control procedure.

face recognition solution algorithm

Face verification (aka face comparison and face matching)

Face verification technology operates on a one-to-one (1:1) comparison basis, also known as face matching. The goal of face verification is to ensure that an individual is the person they claim to be. In practice, this usually involves the combo of an ID + selfie.

Face verification is crucial during KYC-related scenarios, such as creating a bank account or verifying identity during online crypto transactions. Before allowing you to proceed, the app requests you to glance at your phone’s camera. This is face verification in action—a secure, one-to-one comparison that confirms you are the rightful account holder.

Modern border control systems also rely on robust face verification solutions to maintain security and streamline processes. Automated eGates, for example, use face verification to match travelers' faces with their passport photos. This speeds up the passport control and boarding process, and minimizes potential human errors.

face verification example

Smart face verification algorithms can recognize a person even if their appearance has changed.

Combining face detection, recognition, and verification creates the ultimate security blend

Although face detection, recognition, and verification are sufficient on their own, their combined strengths can enhance security and effectiveness in verifying identities.

The process begins with face detection, which identifies the presence of a face within an image or video. This step triggers the subsequent, more specific technologies. 

Once a face is detected, face verification comes into play to perform a one-to-one (1:1) comparison. This could involve matching a live selfie of a person against their passport photo (also read about liveness checks), or running additional checks of passports, such as comparing portraits contained in different parts of the document: the visual zone, RFID chip, and the ghost photo or any other secondary photo, visible under different lights. The submitted image can also be verified against the existing user’s profile in any database. 

Following the verification stage, face recognition technology performs a search through your database on a one-to-many (1:N) basis to match the detected face against a pool of faces. This is particularly useful in situations when your customers want to perform some high-stakes operations, e.g., withdraw a hefty sum. Face search will help you make sure it’s a legitimate customer who conducts the transaction, not an imposter or fraudster of any sort.

Together, these technologies form a comprehensive identity verification system that significantly reduces the risk of fraud.

How to implement all of the above in your workflows

Implementing face detection, recognition, and verification technologies into your procedures has never been simpler, thanks to Regula Face SDK. This solution fits seamlessly into existing systems, offering a solid platform for biometric verification that works across various user devices.

Regula Face SDK is specially designed to differentiate genuine users from fraudsters, ensuring that access is granted only to credible individuals. This enhancement to your security measures includes several key features:

  • Fast and accurate biometric checks: Utilize the power of AI to ensure instant yet precise identity checks.

  • Comprehensive face verification: Verify identities against documents with unmatched accuracy.

  • Active and passive liveness detection: Sophisticated algorithms verify the physical presence of an individual, thwarting spoofing and impersonation attempts.

  • Cross-device compatibility: Whether your users are on smartphones, tablets, or desktops, face verification remains seamless and efficient.

  • Face Search: Run in-depth checks across your internal and external databases.

Have questions on how exactly it can work for your business case? Submit the form to talk to Regula’s experts.

Regula Face SDK

Make face verification fast and secure

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