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Federal agencies are expanding their use of facial recognition and related systems, but the available figures do not add up to a government-wide facial-recognition budget. The clearest evidence comes from agency software licenses, broader AI and border-surveillance funding, and contracts for immigration-enforcement systems—spending categories that should not be mistaken for facial recognition alone. Government Accountability Office (GAO) reports also show that training, privacy protections, and oversight have not consistently kept pace with use.
How much are federal agencies spending on facial recognition?
No single public government-wide total is established by the available GAO and Associated Press (AP) reporting. The figures below describe different kinds of federal spending and authorizations; only the software license is specifically identified as facial-matching software, and its dollar value is not stated.
| Figure | What it covers | What it does not establish |
|---|---|---|
| More than doubled from 2023 to 2024 | Federal agencies’ reported use of AI, according to GAO in 2026. | This is growth in AI use, not a facial-recognition spending increase or a dollar total. |
| Approximately $1.7 billion | Congressional appropriations for AI efforts across the government, as reported by GAO in 2026. | It is not a facial-recognition-only appropriation. |
| Dollar value not stated | A firm-fixed-price order DHS used to license facial-matching software for department-wide use, including traveler identity verification at airports, according to GAO in 2026. | The GAO account does not state the order’s value. |
| More than $2.7 billion | Congressional authorization for CBP to build out border-surveillance systems and add AI and other emerging technologies, reported by AP in 2026. | This is a broader border-surveillance authorization, not a facial-recognition budget. |
| $30 million | Payment to Palantir to extend a system designed to locate people flagged for deportation, reported by AP in 2026. | This is an immigration-enforcement system figure, not a facial-recognition-only contract. |
These figures are not directly comparable: they include reported usage growth, appropriations, an authorization, a software order, and a system extension. They show activity across connected technologies and missions, but do not support adding the amounts together as facial-recognition spending.
Which federal agencies use facial recognition?
GAO’s reports describe use across the Departments of Justice (DOJ) and Homeland Security (DHS), as well as law-enforcement access to systems operated by other entities. The counts below come from different surveys and samples; they should not be treated as one census.
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| GAO review | Reported use | Scope and important distinction |
|---|---|---|
| 2022 survey of 24 large federal agencies | 18 agencies said they used facial recognition for at least one purpose. | Common purposes included digital access and domestic law enforcement. |
| 2022 survey of 42 federal law-enforcement agencies | 14 reported using facial recognition for criminal investigations; 13 did not track employee use of non-federal systems. | This is a law-enforcement-agency sample, not the survey of 24 large agencies. |
| 2024 review of seven selected DOJ and DHS law-enforcement agencies | All seven reported using systems owned by other entities; three also owned systems. | Other providers included state, local, and nongovernment entities. |
The distinction between owning a system and using someone else’s matters: federal use may occur through access to a state, local, or commercial service rather than a federal agency’s own database or software. GAO’s reviews identify criminal investigations, digital access, and traveler identity verification among the purposes reported. The DHS policies GAO examined included technologies used by CBP, Immigration and Customs Enforcement (ICE), and the Secret Service.
What is Mobile Fortify, and are CBP and ICE using facial recognition in the field?
AP reported in 2026 that DHS has been using Mobile Fortify, an app made by NEC, to compare face scans captured by agents with “trusted source photos” for identity verification. This is time-sensitive reporting about DHS use; it should not be read as a complete public accounting of every agency, location, or encounter using the app.
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AP also reported that a lawsuit brought by Illinois and Chicago alleged more than 100,000 Mobile Fortify field uses. That number is a litigation allegation, not an independently audited federal statistic. DHS told AP that technology supports investigations and law-enforcement activity while respecting civil liberties and privacy interests. Former CBP senior adviser Dan Herman warned AP: “Everyone should be very concerned about the potential that this data could be weaponized for improper purposes.” The two statements represent the agency’s position and a former official’s concern, respectively; neither independently verifies the app’s usage count or resolves how safeguards operate in practice.
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AP’s account also describes expanded DHS data integration. The available reporting does not establish a complete, independently verified inventory of what information is integrated, which agency personnel can access it, or how long data is retained. Those specifics matter when evaluating the privacy impact of field identification.
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What privacy and civil-rights concerns have oversight reviews identified?
Facial recognition can turn an image into a possible identity match, but the consequences depend on how people use and verify that match. GAO reported that civil-rights and civil-liberties advocates warn that overreliance can contribute to the arrest or prosecution of innocent people and chill First Amendment activity. These are identified risks, not a claim that every use produces those outcomes.
- Training gaps: GAO’s 2024 review found that all seven selected agencies initially used covered nongovernment facial-recognition services without requiring users to complete training. By April 2023, only two of the seven required training.
- Untracked external-system use: GAO’s 2022 work found agencies often lacked mechanisms to track employee use of non-federal facial-recognition systems. Without that information, oversight may not capture the full range of searches made using outside services.
- Privacy and accuracy assessment: GAO found agencies had not assessed related privacy and accuracy risks in its earlier review. In 2024, it found DHS policies for technologies used by CBP, ICE, and the Secret Service did not always address key privacy protections.
- Bias review: GAO reported that DHS lacked procedures to assess bias risks for all detection, observation, and monitoring technologies. That finding concerns a broader class of technologies, not only facial recognition.
- Notice and choice: For CBP’s Biometric Entry-Exit program, GAO recommended complete privacy notices, clear opt-out information, partner privacy audits, and performance monitoring. CBP later reported improvements to signage, its website, and opt-out information.
Biometric Entry-Exit is a specific traveler-processing program, so its notice and opt-out findings should not be assumed to describe every law-enforcement or field-use setting. The core oversight questions differ by mission: travelers need understandable notice and meaningful information about opting out where applicable; investigations need controls on searches, reliable review of possible matches, and records that let oversight bodies assess use.
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Have safeguards improved, and what remains unresolved?
Oversight has prompted policy and public-information changes, including the CBP signage, website, and opt-out improvements reported after GAO’s recommendations. But the GAO findings also identify gaps in training, tracking, privacy protections, and bias-assessment procedures. A change in written policy or signage is not, by itself, evidence that a safeguard is consistently implemented or effective.
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In its 2024 review, GAO said the seven selected agencies had made about 60,000 searches using covered facial-recognition services before training requirements were in place. The figure refers to searches by those selected agencies using the covered services; it is not a count of people identified, arrests, field app uses, or all federal facial-recognition searches.
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To judge whether safeguards are working, oversight needs more than a list of systems. Useful evidence includes which services employees can query, whether training is completed before access, how searches and possible matches are logged, whether notices and opt-outs are provided in the relevant setting, and whether agencies test performance and assess privacy and bias risks. Public reporting that answers those questions would make it easier to distinguish a formal safeguard from one that is documented and operating.
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