Remove Your Face From Facial-Recognition Search Engines (PimEyes and Reverse-Image)

Before any opt-out

Find the surface before you file.

Facial-recognition exposure is usually two problems stacked together: the engine that finds your face, and the site that actually hosts the image. Treat those as different surfaces.

If the result is intimate, nude, AI-generated, or tied to a threat, the safety boundary changes immediately: live help first, sensitive files held back, no card-first flow.

Before anything else: is the search surfacing intimate images?

Face-search engines are increasingly used to hunt down sexual or intimate images of a specific person — it is a common move in sextortion and image-abuse cases. If a reverse-image or face search is returning nude, sexual, or otherwise intimate images of you (real or AI-generated), stop here. Do not fill out the assessment form on this page.

That situation is handled differently. Use the button to reach a person directly; you do not need to start with checkout, and we will not handle an intimate-image case through a self-serve form.

Everything below this band is for non-intimate facial-recognition opt-out and suppression — your ordinary photos showing up in face-search results. If that is what you are dealing with, read on.

What this page is, and what it is not

AboutUs is a content-removal and online-reputation agency. We are not a law firm, and nothing on this page is legal advice. This page is educational — it names the public boundaries and their limits so you can see the line before authorization. If you want repeat follow-up, source separation, and case maintenance handled as managed work, we put that inside a written agreement.

One honest warning before you start: no one can truthfully promise total erasure from every search engine and every website. Faces get re-indexed, new photos get uploaded, and new engines launch. Anyone selling certainty before review is overselling. A real service defines the reviewed item, intended outcome, visibility-work limits if any, and refund term in writing before AboutUs work begins, and is candid about limits. Keep that filter on as you read everything below — including the parts about us.

How face-search engines actually work.

A reverse-image search matches a specific picture. A face-search engine is different and more invasive: it builds a mathematical signature of your face (a 'faceprint') and then finds other photos of the same face anywhere it has crawled — different clothes, different years, different websites. PimEyes is the best-known consumer example; Clearview AI is the best-known law-enforcement one; FaceCheck.ID and a rotating cast of clones operate in between.

Two facts shape every realistic plan. First: most of these engines do not host the photos. They index pictures that live on other websites and return links to them. Second: because face-matching is probabilistic, not exact, one visibility request rarely catches every image of you. That is why source work, index work, and follow-up should stay separated.

PimEyes and consumer face-search boundaries.

Consumer face-search engines may offer visibility or privacy requests, but the requirement set and reviewer posture can change. Public copy should not become a submission packet. The important boundary is that index suppression does not delete the photos from the original websites where they live.

Treat this as removing the index card, not the book. The client-record work is deciding which source pages, index matches, and repeat checks belong in the same scope.

Clearview AI and biometric privacy rights.

Clearview AI and similar biometric systems sit closer to privacy-law standing than ordinary search visibility. Rights vary by state, jurisdiction, and the legal basis available to the person requesting review.

Be realistic about boundaries. These programs generally operate within narrow eligibility rules, and outside specific legal regimes the rights can be thinner. The public page can name that ceiling; eligibility, proof posture, and follow-up stay in the client record.

FaceCheck.ID and the clone problem.

FaceCheck.ID and similar engines each maintain their own database, so action at one engine does nothing at the others. New lookalike services also appear over time.

The practical reality is that this is recurring maintenance, not a one-time fix. Public copy can say the problem repeats; the actual engine-by-engine posture, receipts, and follow-up cadence are managed work.

Google is a visibility layer, not the source.

Google is not a face-search engine, but it is often how people discover images. Its eligible removal options can reduce search exposure for certain results, while the original source may remain live.

That distinction matters commercially and ethically. Google visibility, face-search visibility, and source deletion are three different outcomes, and the client record keeps them separate.

Delete-at-source vs. de-listing — and why you need both

There are two fundamentally different moves, and confusing them is the most common mistake we see. Delete-at-source means the photo is removed from the website that actually hosts it — the social profile, the forum, the data-broker page. When the source is gone, every face-search engine and every search index loses it on the next crawl. This is the real fix.

De-listing means asking a search engine or face-search engine to stop returning a result while the underlying photo still exists. It can be the only lever you have when the host will not cooperate. But it is a fallback, never a substitute: the image is still live, still findable by direct link, and can resurface in a different engine. A trustworthy plan goes after the source first and uses de-listing to cover what the source will not.

Two source categories are honestly hard. Genuine news or editorial coverage usually will not come down without the publisher's agreement or a court order, so the realistic offering there is de-listing and suppression, not deletion. People-search and data-broker pages that display your photo next to your details can also require recurring maintenance rather than a one-time promise.

Why opt-out is an ongoing problem, and what managed follow-up adds

Even a clean first pass decays. New photos get posted, old sites get re-crawled, engines tweak their matching, and fresh face-search clones launch. The only thing that holds the line is repeat follow-up — catching new appearances early and keeping source pages separate from index matches.

The hard part is consistency: keeping the file clean, checking the right surfaces, separating source pages from index matches, and knowing when a host will not cooperate. What a service adds is steady case handling and reach without turning every step into an open checklist. The mindset that works is maintenance, not a one-time cleanup.

Where managed face-search work fits

If this is more than you want to manage, this is the part we do for a living: separating index matches from source pages, pursuing reachable source removals, labeling visibility work, and running follow-up for new matches over time. For eligible reviewed items, the service agreement defines the item, intended outcome, visibility-work limits if any, and written refund term before work starts. Timing varies by host, platform, legal boundary, and whether the request is complete — no honest service guarantees a specific outcome.

Start with a client-record check. You tell us what you are seeing and where; we tell you candidly what is realistically removable, what is suppression-only, and what it would take. No card-first pressure, no countdown, and no sensitive file in a public form.