Methodology

How 221B frames public-web reverse face search.

This page explains the search scope, the limits, and the review workflow behind 221B. The product is designed to help users discover publicly available candidate pages and then verify those pages manually.

Search scope

What 221B searches

  • Public profiles, creator pages, and social pages that are already visible on the open web
  • Articles, interviews, blogs, and other crawlable pages where the same face may appear in different photos
  • Candidate source pages that still require manual review before they mean anything

Out of scope

What 221B does not claim to do

  • Private accounts, hidden databases, or gated content
  • Automatic identity conclusions based on a face score alone
  • Guaranteed accuracy on weak input such as heavy blur, masks, group shots, or strong filters

Review workflow

How to interpret a candidate match

  1. 01

    Start with one strong input image

    Use one visible face, front-facing if possible, with stable lighting and minimal obstruction. Better input usually produces better candidate pages.

  2. 02

    Treat the score as a ranking signal

    A confidence score helps prioritise which pages to inspect first. It does not prove that a person has been identified correctly.

  3. 03

    Read the original source page

    Names, usernames, bios, timestamps, page context, and source credibility are what turn a candidate page into something actionable.

Why this matters

Reverse face search is a review workflow, not a shortcut to certainty.

The homepage, blog content, and methodology should all say the same thing clearly: 221B is built to help users inspect public-web matches responsibly.

That means being explicit about scope, limitations, and manual review. It is better for users, and it also makes the site easier for search engines to understand because the content is concrete instead of vague.

Editorial principles

How 221B keeps product copy and guides aligned

  • Keep the product scope explicit: public-web pages only, not private databases or hidden content.
  • Describe confidence scores as ranking signals, not proof of identity.
  • Prefer source-page review, cross-checking, and context over automation-heavy claims.