Face Match vs the Alternatives: Choosing Biometric Verification

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Face matching is a biometric 1:1 comparison that checks whether a live selfie belongs to the person shown on an identity document, addressing impersonation risks that document-only verification and database validation cannot resolve alone. It differs from 1:N face search, which compares a face against an enrolled database to detect duplicate accounts or blocklisted users, while document verification establishes whether an ID is authentic and database checks confirm that an identity record exists. Stronger remote identity-verification flows combine document checks, face matching, and liveness detection to ensure the document is genuine, the applicant matches its portrait, and the selfie represents a physically present person rather than a spoof. The text presents these methods as relevant to regulated fintech, crypto, marketplaces, gig platforms, and iGaming, and describes Didit’s hosted workflow, which combines modules in one session; it lists Face Match at $0.05 per check, free Face Search, and a $0.33 core KYC flow including ID verification, passive liveness, face matching, and IP analysis.

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