Face recognition search finds every asset a specific person appears in by matching faces detected in images against reference examples of that person. Once a person is identified in the system, a single search returns their appearances across the whole library — including photos nobody ever tagged with their name.

The DAM use cases are steady and practical. Communications teams need every approved photo of an executive for a press request. Event photographers deliver thousands of frames, and each featured speaker needs their own selection. And when someone leaves the company or withdraws consent, the team must find every image they appear in — a task that is effectively impossible with manual tagging and routine with face search.

Because faces are personal data, how the matching works matters as much as that it works. The responsible pattern is reference-set matching with human confirmation: detected faces stay anonymous until a person deliberately creates an identity and confirms which faces belong to it, and matching runs against those confirmed references rather than the system inventing identities by clustering strangers together. That keeps a human decision at the point where a face becomes a name, which is where consent and data-protection obligations live.

In freedam, face recognition uses exactly this model: detection runs automatically, but faces remain unassigned until a person is seeded with human-confirmed reference examples, and matches are made against that reference set.

Related terms: Visual Search, Metadata