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Records described by CyberScoop show that ID.me warned state partners about the risks of 1:many facial recognition while also telling them it was considering that kind of technology—and later rolled out a feature with the same technical definition. The documents also raise questions about how clearly ID.me distinguished that feature from its public descriptions of 1:1 verification, and how people unable to complete automated checks were affected by long waits for video chats.
What ID.me told states about 1:many facial recognition
In records obtained by the ACLU, ID.me cautioned state partners that 1:many facial matching “casts a much larger net and introduces a higher probability of error.” It also wrote: “It is deeply irresponsible for the media to conflate 1:1 Face Verification with 1:Many Face Recognition.” CyberScoop reported on those materials on February 18, 2022.
The same report said ID.me confirmed that it had told state partners as early as November 2020 that it was considering 1:many facial recognition. In February 2021, the company introduced a feature called “Duplicate Face Detection,” describing it as “a major technological breakthrough in fraud prevention” and “proven remarkably accurate.” CyberScoop reported that the feature had the same technical definition as 1:many facial recognition, although the materials sent to states did not use that label.
Those records document a tension between ID.me’s warnings about 1:many matching and its rollout of Duplicate Face Detection. They do not, by themselves, establish that the company’s 1:1 and 1:many systems were identical in every operational detail.
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How 1:1 verification differs from 1:many matching
| Approach | What is being compared | What the distinction means |
|---|---|---|
| 1:1 face verification | A person’s face is compared with a specific reference image, such as the image on an identity document. | The check asks whether the two images appear to represent the same person. |
| 1:many face recognition | A face is compared against multiple images or records in a broader set. | The search can look for a match across that larger set. ID.me warned state partners that this wider search carries a higher probability of error. |
The terms describe different kinds of matching, not an accuracy guarantee. A statement that a system uses 1:1 verification does not answer whether a separate feature also compares faces against multiple records. In this case, CyberScoop’s account says Duplicate Face Detection had the same technical definition as 1:many recognition, even though ID.me’s state-facing materials did not call it that.
What the records show about verification delays
A 2022 release from Democrats on the U.S. House Committee on Oversight reported that ID.me had contracts with at least 25 state agencies and had received nearly $45 million for identity-verification work. The committee also reported that ID.me told investigators 10–15% of users could not complete automated facial verification and were sent to video chats.
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The committee’s review of ID.me data found waits of four hours or more in 14 of 21 states in April 2021. At an April 2021 meeting with IRS officials, ID.me reportedly described waits as “about 2 hours as of today.” The committee’s account therefore shows a gap between that estimate to the IRS and the longer waits recorded for many state users; it does not establish that every state user waited four hours or more.
What ID.me says about biometric data
ID.me’s biometric notice says the company “will not sell, rent, or trade” biometric information. The notice says images from identity documents and selfies may be used to derive facial geometry, support identity proofing, and compare a person’s biometrics across the ID.me platform to detect multiple wallets or fraud.
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Under the notice, biometric information is generally deleted no later than one year after a wallet is closed. It may be retained for up to three years if associated with a fraud investigation, suspected fraudulent activity, or a confirmed fraud attempt. The notice also says that refusing to provide biometric information may prevent ID.me from verifying a person’s identity or providing requested services. These statements describe the company’s stated policy; they do not establish how long a particular person’s data is retained in an individual case.
Why disclosure and accessibility matter
An ACLU-led coalition argued that ID.me initially claimed to use only 1:1 facial recognition, then acknowledged using 1:many recognition as well. The coalition warned that broader searches raise concerns about bias, privacy, and civil liberties. It also pointed to the camera and internet access needed for remote verification as potential barriers for older people, people with low incomes, disabled people, and other marginalized groups.
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Those are concerns raised by the coalition, not proof that every user experiences the same barrier or that every facial match produces a biased result. CyberScoop also quoted AI researcher Joy Buolamwini saying, “Failing a benchmark test is a red light, but passing them is not a green light.” The point is that benchmark performance alone cannot settle questions about real-world accuracy, subgroup differences, disclosure, or whether people have a practical alternative.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the evidence does—and does not—establish
The records and statements summarized in the 2022 reporting establish that ID.me discussed considering 1:many facial recognition with states, warned them about its greater error risk, and later deployed Duplicate Face Detection, which CyberScoop described as having the same technical definition. They also document the verification failure rate and wait times reported by the House committee, alongside ID.me’s published statements about biometric use and retention.
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They do not provide a single independently validated, current accuracy percentage for ID.me across demographic groups. The material notes subgroup disadvantages in an Oregon study, but does not supply enough detail here to quantify those results. Nor does the company’s description of Duplicate Face Detection as “proven remarkably accurate” constitute an independent validation. Anyone assessing an identity-verification option should look for clear disclosure of when 1:many matching is used, evidence about demographic performance, a workable non-biometric route, accessible fallback support, and specific retention and deletion terms.
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