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Facial recognition technology (FRT) analyzes a facial image to check whether it matches a claimed identity or to search for a person among many records. Those are different tasks with different error risks. Companies develop and sell systems for uses ranging from identity checks to surveillance, but no single accuracy score can tell you whether a particular deployment is reliable, appropriate, or privacy-protective.
What is facial recognition technology?
Facial recognition software processes an image of a face and compares it with another image or a collection of images. Its result is a similarity assessment, not proof by itself that two images depict the same person. The system’s output is affected by the task, the images, the comparison threshold, and the records available to search.
Verification: one-to-one comparison
Verification asks whether a person is the individual they claim to be—for example, whether a presented face matches a reference image associated with an account or identity record. It compares one image with one enrolled identity.
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Identification: one-to-many search
Identification searches an image against a collection to find a possible match. A search can return a candidate or candidates for further assessment; it does not, on its own, establish identity. Because the system searches many records rather than checking one claimed identity, its results should not be treated as interchangeable with verification results.
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Which companies make facial recognition software?
The market includes developers of recognition algorithms and vendors that incorporate those algorithms into products or services. Government agencies and commercial organizations may use such systems for identity or security functions. The specific product, deployment, and task matter more than the general label “facial recognition.”
The National Institute of Standards and Technology (NIST) runs the Face Recognition Vendor Test (FRVT), which independently evaluates commercial and prototype algorithms. NIST describes FRVT as a testing program, not a complete directory of companies or an endorsement of a vendor. Its program page, updated in 2025, summarized demographic-effects testing covering nearly 200 algorithms from nearly 100 developers and more than 18 million images of more than 8 million people. Those figures describe the scope of that testing, not the size of the market or the number of people in live deployments.
For a concrete example of scrutiny of company claims, the Federal Trade Commission (FTC) finalized an order in January 2025 involving IntelliVision. The order restricts specified accuracy, bias, training, and anti-spoofing claims unless they are supported by competent and reliable testing. That case is not a ranking of vendors; it illustrates why a company’s marketing language should be checked against evidence for the particular system and use.
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What is facial recognition used for?
Government agencies have used FRT for identity and operational purposes. The U.S. Commission on Civil Rights examined uses by the Departments of Justice, Homeland Security, and Housing and Urban Development in a report discussed in its September 2024 release. Commercial uses include identification and security functions; the Government Accountability Office (GAO) has also discussed commercial use and related privacy concerns.
A use case alone does not establish that a deployment is accurate or justified. Assessment depends on how the system is used, who or what is included in its reference collection, the quality of the captured image, and whether a person reviews the result before action is taken.
How accurate is facial recognition?
There is no single accuracy figure that applies to every facial-recognition system or setting. NIST’s FRVT evaluates verification and identification separately and examines performance under defined test conditions. Results should be tied to the specific algorithm and version tested, the image conditions, the comparison threshold, and the operational context. A result from one test cannot automatically predict performance in a different deployment.
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Two types of error are especially important to understand:
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- False non-match: the system fails to match images that do depict the same person.
Changing a system’s threshold can alter the balance between these errors. A deployment therefore needs to consider the consequences of each kind of mistake, not just an overall performance figure.
Performance can also differ across demographic groups. NIST measures demographic effects in its evaluations, and GAO has described concerns about differential error rates. A claim that a system is “bias-free” or “the most accurate” is not meaningful without specific, relevant evidence. The FTC’s January 2025 IntelliVision order is a case-specific example of limits on specified claims that lack competent and reliable testing.
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What are the privacy and civil-rights concerns?
Image collection, notice, and tracking
GAO has described concerns about commercial collection and use of facial images, including identifying people in images they may consider anonymous, tracking, and differences in company privacy policies. Whether a person receives notice or has meaningful choices depends on the circumstances and the policies and laws that apply. GAO’s discussion of federal privacy law reflects the date of its report and should not be read as a statement of current law in every jurisdiction.
Consequences of errors and unequal performance
A false match can have serious consequences when a result prompts an investigation, denial of access, or another consequential action. Uneven performance can distribute those risks differently across groups. Human review, clear limits on when results may be acted on, and a way to correct errors are therefore important parts of deployment—not substitutes for testing.
Oversight and live use
In its September 19, 2024 release, the U.S. Commission on Civil Rights said that meaningful federal guidelines and oversight for responsible FRT use had lagged behind real-world deployment. That was a dated assessment of federal use, including the agencies its report examined; it does not describe every state, local, or foreign legal framework.
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NIST’s January 2024 framework for passive live facial recognition highlights proportionality, human rights, privacy, and appropriate performance metrics. These considerations are particularly important when a system scans people who have not actively presented themselves for an identity check.
Enforcement actions also show that safeguards and evidence matter. In December 2023, the FTC announced action involving Rite Aid’s facial-recognition surveillance and alleged failures to assess accuracy and use reasonable safeguards. The announcement concerned that case and its allegations; it should not be generalized into a finding about every deployment.
How should an organization evaluate a facial-recognition system?
Evaluation should begin with the decision the organization wants the system to support, not with a vendor’s headline accuracy claim. The following checks connect technical performance with the way a system will affect people:
- Define the task: establish whether the system will verify a claimed identity or search a collection to identify someone.
- Check relevant test results: identify the algorithm and version tested, the test conditions, image quality, and threshold. Look for results that match the proposed use rather than relying on a general score.
- Examine error patterns: consider false matches and false non-matches, including demographic performance under relevant conditions.
- Review the data: understand how images are collected, what the reference database contains, how it is maintained, and who may access or use it.
- Set human-review rules: specify when a person must review a result and what independent evidence is required before consequential action.
- Provide notice and oversight: establish appropriate transparency, authorization, limits, and accountability for the deployment.
- Plan for incidents: define how to challenge or correct a mistaken result, investigate failures, and respond when safeguards do not work.
NIST testing can help compare algorithm performance, but it does not settle the separate questions of whether a particular use is proportionate, lawful, or well governed. The GAO’s privacy analysis, the Civil Rights Commission’s federal assessment, and NIST’s live-use framework address aspects of those wider deployment decisions.
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