Face Duplicity check (1:N)
Face duplicity check searches face from the new digital identity record against all the existing Customer records — a 1:N search. It answers a fraud-and-identity question that document checks alone cannot: have we seen this face before, and under what identity?
What it detects
- Duplicate accounts — the same person onboarding again with a different document.
- Name change - the same person onboarding again with a changed name
- Fraudsters — a fraudster with multiple forged ID documents.
How it works
- When a workflow reaches the duplicity check step, the face from the digital identity is searched against the watchlists — the Customer watchlist and the Blocklist, and the Concurrent watchlist of identities waiting for review.
- The search returns up to 10 candidates, re-ranked by the similarity score and the watchlist it came from.
- The top result and its score determines the outcome.
Outcomes
The result distinguishes where the match was found:
| Result | Meaning |
|---|---|
| No match | The face is new. |
| Customer match | Matches an existing Customer — the attempt can be merged or sent to review. |
| Blocklist match | Matches a blocked person — the attempt is rejected. |
| Concurrent | Matches another verification happening right now (see below). |
Concurrent-onboarding protection
Two sessions for the same person running at the same time could each create a separate Customer. To prevent this, an in-flight applicant's face is briefly tracked while its verification is undecided, so a simultaneous second attempt is flagged as concurrent rather than slipping through as unique. Once the verifications resolve, this temporary tracking is cleaned up automatically.
Combining with document data
Face duplicity check is most powerful alongside document duplicity check: comparing the face signal with the name + date of birth signal is what separates a returning customer from a stolen ID, a twin, or a namesake. See the matrix in Identity Verification.