Understanding False Acceptance Rates (FAR) in Biometrics
Blog post from Didit
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False Acceptance Rate (FAR), also called False Match Rate, measures the probability that a biometric system wrongly grants an unauthorized person access, making it a central indicator of fraud and security risk. Even a low FAR can create substantial exposure at large scale, potentially leading to account takeovers, financial losses, data breaches, reputational harm, and compliance failures, particularly when attackers use photos, videos, masks, or deepfakes. FAR must be balanced against False Rejection Rate (FRR), which occurs when legitimate users are denied access, with the appropriate trade-off depending on whether an application prioritizes security or convenience. FAR is influenced by biometric modality, matching algorithms, data quality, spoof-detection capabilities, and configured sensitivity thresholds. Didit presents its AI-based 1:1 face matching, passive and active liveness detection, configurable thresholds, AML screening integrations, APIs, and no-code console as tools intended to reduce false acceptances while allowing organizations to tailor verification workflows to their risk requirements.
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