In the UK, one can often come across the term Identity Document Validation Technology (IDVT) when looking at official guidance for digital identity checks. However, outside the UK, this nomenclature is rarely seen, with phrases like “document authentication” or “document verification” being more prevalent.
The question is: is there any practical difference between those terms? If so, what is it? And what does IDVT entail in the first place?
Read further to find out.
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What is IDVT?
Identity Document Validation Technology is the instrument used to establish the authenticity of documents presented for ID verification purposes. It typically combines a capture device (a smartphone camera, webcam, or dedicated reader) with software that verifies and compares personal data from visual zone, MRZ, NFC, checks document patterns and security features, then aggregates the results to make a decision.
This operation is important because it’s the backbone of many of today’s identity verification procedures. Whenever a user submits a photo of a document, such as a passport, ID card, or driver’s license, the service needs to validate it first before proceeding to the next onboarding step. Without this technology, which is fully automated, teams would rely heavily on manual checking or accept more risk than they realize.
Why the UK uses the term so often
IDVT often shows up in UK conversations because it is used directly in government guidance. It is commonly referenced alongside the Digital Identity and Attributes Trust Framework, which is described as a set of rules and standards for what good digital identity verification should look like.
Outside the UK, vendors often use “ID document authentication” and “ID document verification” for what UK guidance tends to label IDVT. In practice, the terms overlap heavily, and many teams use them as synonyms (more on this later).
How an IDVT check works in practice
An IDVT check is easiest to understand as a pipeline. Each step produces certain signals, and the final result is a decision with supporting evidence that can be reviewed later.
Capture and scan quality
Capture is where many checks can fail, and not even because the document is fake, but because the image is unusable. Glare on laminate, blur, occlusion and cropped edges can all make input “weak,” which in turn will make every later step unreliable; so most IDVT stacks will prompt a retake in such cases.
It’s also worth mentioning that there are two distinguishable capture styles:
Passive capture: The user submits a still image (or a basic scan) and the system works with what it gets.
Active capture: The flow asks the user to do something that reveals more evidence, such as tilting the document, changing distance, or recording a short video segment. Active capture is often used when the goal includes checking liveness and dynamic security elements that behave differently as the angle changes.
Document type detection
Once the captured image looks usable, the system identifies the document type: country, series, layout and features it should have. This is important because document designs often change, and different series can have different security feature sets even within the same country and document category.
A typical IDVT engine compares the submitted image against a template library; and the closer the match, the more precise the checks can be. For international identity checks, this step is one of the biggest determinants of reliability.
For example, Regula Document Reader SDK uses the largest such library in the world, with more than 16,000 document templates from 254 countries and territories.
Data extraction and cross-checking
Extraction pulls identity data from the zones and channels available: the visual inspection zone (printed fields), MRZ, barcodes, and, where available, RFID chips.
The key point is not extraction by itself, but cross-checking. That is where ID document validation really kicks in:
If MRZ check digits fail, it might be a capture issue, but it can also be a tamper signal.
If barcode data conflicts with what is printed, the document evidence is inconsistent.
If chip data does not match printed details, that is a high-impact discrepancy.
Authenticity signals
A lot of remote fraud is not necessarily a counterfeit, but a presentation attack: a printed scan, a screen replay, or an injected video or image stream. That is why some IDVT workflows include document-side liveness-style checks aimed at confirming a physical document is being presented during capture.
A word on terminology
There is a misconception that such liveness detection is meant purely for face (selfie) checks. However, document liveness also exists: it relies on dynamic security elements that are difficult to reproduce in static images, such as features that change appearance with angle or light. They are especially useful when a chip read is not available, because the system has fewer strong signals to work with.
Decisioning and review
No matter how good automation is, some cases will be uncertain: rare document series, borderline capture quality, or mixed signals. That’s why many systems combine the outcomes of individual checks into an overall score or decision, often with reason codes that explain what failed or what looked suspicious.
That makes borderline cases faster to review and helps organizations tune their policies without guesswork.
Differences between related terms: Validation, verification, and authentication
It’s worth mentioning that many people use these labels interchangeably (more on that below). One of the more reliable ways to keep the language straight is to map it to the three questions your system must answer at different points in the journey:
Is the document itself credible?
Is the person presenting it the rightful holder (or does the evidence support the claimed identity)?
Can the same user get access again later without re-checking identity documents?
Validation: Document-focused checking
In most IDV contexts, ID validation effectively means document validation. It focuses on the document as an object and the data inside it, not on the person who submits it.
The question is: does this identity document look like a genuine issued document, and does its content stay consistent across the sources the system can read?
In practice, validation usually includes three layers:
Document type recognition. The system identifies the document class and series so it knows what “normal” looks like for that exact document and what security elements should be present.
Authenticity and integrity signals. These checks look for indications of counterfeit production or alteration. What’s possible depends on capture: a still photo supports fewer checks than guided capture, and a dedicated reader supports more, including UV/IR inspection and chip-based confirmation where available.
Data extraction plus cross-checking. The system reads what it can from printed fields and machine-readable sources (MRZ, barcodes, RFID/NFC chip). The point is not “getting the fields,” it is detecting mismatches and anomalies that suggest a capture issue or tampering.
Here are two things to keep in mind:
What ID validation can confirm: The document looks credible based on the evidence.
What it cannot confirm: That the person presenting it is the rightful holder.
Verification: Claim-focused checking
Identity verification moves past the document and tests the claim: does the evidence support that this person is who they claim to be? It is the step that tries to close the gap between “credible document” and “credible claimant.”
Verification often starts with validated document evidence, then adds checks that tie the document to the person present in the session. Common components include:
Holder linking: Comparing the document portrait to a selfie or live capture to reduce the risk of someone using a stolen document.
Capture integrity controls: Checks aimed at reducing replay, injection, or other ways to submit someone else’s media instead of genuine capture.
Context-driven corroboration: Depending on the use case, additional signals may be used to support the identity claim.
The simplest way to frame identity validation vs verification is this: validation evaluates the document as evidence, verification evaluates the identity claim and whether the evidence belongs to the person presenting it.
Authentication: Access-focused checking
Authentication is the step used after onboarding when a user returns and needs access again. It is not trying to re-prove who the person is in the onboarding sense. It is answering a narrower, operational question: can this user prove they should get access to this account right now?
Authentication typically relies on authenticators such as passkeys, device-bound credentials, authenticator apps, or other possession and knowledge factors. A key difference is that authentication is designed to be repeatable and low-friction, so it usually avoids re-checking identity documents.
Identity documents may reappear only when something has changed in the risk picture, for example account recovery, suspicious activity, or a policy that requires periodic re-verification.
Where the terms blur
That said, the above terms are used rather inconsistently across markets and product categories, so relying on labels alone is risky and may create confusion. Some examples of interchangeable use of these terms include, but are not limited to:
“ID verification” as an umbrella phrase for document checks plus selfie matching OR only reading fields from a passport photo.
“Document validation” as checking “not expired and fields present,” with no genuine assessment OR a full integrity check across VIZ, MRZ, barcode, and chip.
Some practical tips
If you want language that stays as precise as possible:
Don’t rely on labels alone: The same term can mean different scopes depending on the vendor or team.
Emphasize the specific checks you are interested in: When doing research, focus on what exactly must be assessed and what will count as evidence.
If you need to confirm the document is genuine and intact, say that explicitly:
Name the evidence sources you expect: template match, security-feature signals, cross-checking printed fields with MRZ/barcode/chip, and reader-based checks (UV/IR, RFID/NFC) when relevant.If you need to confirm the person is the rightful holder, also say that explicitly:
Mention holder linking (1:1 match or portrait-to-selfie comparison) and capture integrity controls that reduce replay/injection risk.Remember that “authentication” is an overloaded term: In identity and access management (IAM), it usually means account access; in document checking it is often used to mean document authenticity, especially with passport reader devices.
A final word on IDVT
In plain business terms, IDVT is a way to turn a submitted document into evidence you will need to use during ID verification checks. Good identity document validation technology raises confidence when the document is genuine, flags the cases that need attention, and leaves a trail that auditors can understand.
