Facial recognition is AI that detects faces in a photo and groups images of the same person together across an entire library — so once one photo of someone is labeled, the system can suggest that label on every other photo containing that same face, without a person manually tagging each one.
In plain English
Tagging people by name is one of the most tedious parts of manual keywording — the same handful of colleagues, clients or brand ambassadors show up across thousands of photos, and typing their name onto each one individually doesn't scale. Facial recognition automates that specific task: the system detects a face, groups it with other photos containing what it judges to be the same face, and once a person confirms one group is "Jane Smith," every other photo in that group inherits the name.
This is narrower than general auto-tagging, which suggests keywords for objects and scenes broadly. Facial recognition is specifically about identifying and grouping people, and it usually comes with its own review step — confirming a face-group belongs to the right person — separate from the general keyword-approval queue.
It's also worth knowing what it doesn't do by default: recognizing that two photos show the same face is different from actually knowing that face's name. The system can group faces on its own, but a human still has to attach the correct name the first time a given person appears, unless the tool imports names from an existing contact list.
Why it matters in a DAM
For libraries built around people — event photography, brand ambassador programs, staff photo archives, stock libraries organized by model — facial recognition is often the single biggest time-saver available, because manually tagging every face across tens of thousands of images is not realistic at any team size. It also matters for a less obvious reason: privacy and consent. Grouping and identifying real people's faces is a meaningfully more sensitive use of AI than tagging generic objects, and how a vendor handles opt-out, data retention and regional regulation (like GDPR's treatment of biometric data) should factor into the decision as much as raw accuracy.
Buyer’s test: ask specifically how the tool handles a request to remove someone's face data entirely, not just delete their name tag. Facial recognition often stores a biometric-style "face signature" separately from the visible tag, and some regions' privacy law treats that signature itself as sensitive personal data requiring its own deletion path.
Related terms
See it in action
Our best DAM with face recognition ranking tests grouping accuracy and privacy controls side by side across four tools. For one implementation in depth, see our Canto review.