Buyer guide

    How does face recognition work across different faces and conditions?

    Face recognition performance varies with camera quality, lighting, pose, image age, thresholds and population. FaceTagr's engine is benchmarked by NIST FRTE, but a benchmark is not a guarantee for every deployment. Test representative users and working conditions in a pilot, measure false matches and missed matches, and review exceptions before rollout.

    Last reviewed: 18 October 2026 · FaceTagr team

    What affects the recognition result?

    FactorWhat to test
    LightingDaylight, dim rooms, backlighting and your actual camera position
    Capture qualityResolution, motion blur, pose and lens cleanliness
    Reference imagesImage age, enrolment quality and any permitted updates
    PopulationRepresentative participants and differences in error rates
    ThresholdsFalse-match and missed-match rates at your chosen settings
    LivenessGenuine users and photo or screen replay attempts

    How should you reduce deployment risk?

    Step 1

    Review the specific NIST test, task and thresholds rather than a universal accuracy percentage.

    Step 2

    Run a pilot with representative participants, appropriate permissions and a human fallback.

    Step 3

    Measure genuine-user completion, false matches, missed matches and exception resolution.

    Step 4

    Test offline behaviour and later sync on the phones, tablets or CCTV you intend to use.

    Plan a pilot around your workflow

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    Sources

    Product capabilities describe FaceTagr's supported workflows, not third-party endorsement. This page is general information, not legal advice. Check your obligations with your data protection officer or adviser.

    Frequently Asked Questions

    Is one accuracy number valid for every population?

    No. Results depend on the task, threshold, gallery and image conditions. Do not infer demographic performance from a headline score.

    Can it work without a network?

    Yes. FaceTagr's mobile SDK supports fully offline 1:1 verification, 1:N identification and passive liveness on Android and iOS. Reporting and integration need a connection when records are synchronised.