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passive liveness is invisible but puts more weight on the model.","The attack class matters: printed photos, screen replays, masks and deepfakes are each caught by a different signal.","In practice a hybrid flow wins: passive by default, active only when the score looks weak.",[30,33,37,39,45,60,63,65,71,75,78,80],{"kind":31,"text":32},"paragraph","A face recognition model can be flawless and still worthless if what stands in front of the camera is a photo on a phone screen rather than a living person. That makes liveness, not recognition, the most fragile part of a biometric flow.",{"kind":34,"id":35,"text":36},"heading","two-approaches","Two approaches, two different costs",{"kind":31,"text":38},"Active liveness asks the user for a specific action: turn your head, blink, follow a dot on screen. Passive liveness asks for nothing — it analyses frame texture, light distribution, depth cues and micro-movement.",{"kind":40,"items":41},"list",[42,43,44],"Active: harder for an attacker, but every extra step costs 4–9% of users during enrolment.","Passive: completes in 1.2 seconds on average and stays completely invisible to the user.","Hybrid: passive runs first, and the active step is requested only when the confidence score is low.",{"kind":46,"items":47},"stats",[48,51,54,57],{"value":49,"label":50},"99.6%","Liveness accuracy",{"value":52,"label":53},"1.2s","Average check time",{"value":55,"label":56},"4","Attack classes covered",{"value":58,"label":59},"40M+","Attempts verified",{"kind":34,"id":61,"text":62},"attack-surface","Which attack hits which barrier",{"kind":31,"text":64},"It helps to split presentation attacks into four classes. Each class is caught by a different signal, which is exactly why no single model closes all of them.",{"kind":40,"items":66},[67,68,69,70],"Printed photo: paper texture and the way light reflects are immediately visible to a passive model.","Screen replay: moire patterns, frame rate and display brightness give it away.","Silicone mask: it is the absence of micro-expression, not a depth map, that reports the problem.","Deepfake stream: this needs camera-level attestation plus temporal consistency analysis.",{"kind":72,"text":73,"cite":74},"quote","The liveness question is not «is this person alive?». It is this: did the signal from the camera come from a real person, in real time?","RS-ID biometric core team",{"kind":34,"id":76,"text":77},"choosing","What to choose for your integration",{"kind":31,"text":79},"If your flow is mass enrolment, start passive — conversion decides here. If the flow carries real risk, such as a large transfer or signing a document, making the active step mandatory pays for itself.",{"kind":81,"title":82,"text":83},"callout","Practical note","Do not freeze the confidence threshold at a single number. 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