Face Matching Algorithms: The Core of Secure Identity Verification

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Face matching systems use AI to detect faces, identify facial landmarks, generate numerical embeddings, and compare live captures with reference images to produce similarity scores for identity verification, while liveness detection helps distinguish real people from photos, videos, masks, and deepfakes. Passive liveness evaluates biological cues without user interaction, whereas active liveness asks users to perform actions and may provide greater protection against sophisticated spoofing. These technologies support KYC onboarding, passwordless authentication, age checks, fraud and duplicate-account detection, account recovery, and physical access control. Didit presents an identity-verification platform offering 1:1 face matching, passive and active liveness detection, 1:N face search, age estimation, and biometric authentication; it states that its active liveness system is iBeta Level 1 certified and 99.9% accurate, and promotes free usage tiers and pay-per-success pricing.

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