What is liveness detection?
Liveness detection is the set of techniques used to establish that the face presented to a camera belongs to a live human being physically present at that moment — not a printed photograph, a screen replay, a mask, or a synthetically generated video. In standards language the discipline is called presentation attack detection.
It exists because face matching alone is indifferent to what it is looking at. A face-match engine will happily confirm that a photograph of the right person is the right person, which is exactly the weakness an attacker exploits.
Active and passive approaches
Active liveness asks the user to do something — blink, turn their head, follow a moving target, or speak a prompted phrase. It is easy to explain and produces clear evidence of interaction, but it adds friction and excludes some users with disabilities or on poor connections.
Passive liveness makes the determination from a single capture or a short clip without instructions, analysing signals such as texture, depth cues, reflection and screen artefacts. It is far smoother for the user, and it moves the burden onto the quality of the model.
In practice most production systems combine the two: passive first, with active challenges escalated only for borderline scores or higher-risk journeys.
The attacks it has to withstand
- Print attacks — a photograph, sometimes with cut-out eyes or bent paper to simulate depth.
- Replay attacks — a video played back on a phone or monitor.
- Mask and 3D attacks — silicone or resin masks, which defeat most texture-only approaches.
- Deepfake and injection attacks — synthetic video fed into the capture pipeline, bypassing the camera entirely. This is the fastest-growing category, and it is why capture integrity — attesting that frames came from a real device sensor — now matters as much as the model itself.
How it relates to Video KYC
In a V-CIP session, liveness is established partly by a human official asking unscripted questions in real time — a deliberately low-tech and quite robust control. Automated liveness serves the self-service flows that sit before that step, and its purpose is to keep spoofed applicants out of the funnel rather than to replace the official’s judgement.
As with name matching, the output is a score meeting a threshold, and the threshold is a policy decision: too strict and you reject genuine customers in bad lighting, too loose and you admit printed photographs.
Related terms
Verify it with Veriqos
Support your face and liveness checks with independent record-level verification:
- Digilocker Verification API — Verify your customers’ government documents instantly through Digilocker with Veriqos’ Digilocker Verification API — real-time verification through the customer’s Digilocker account.
- Passport Number Verification API — Authenticate passport details in real time with Veriqos’ advanced Passport Number Verification API — verifying name, passport number, expiry date, and nationality in seconds.
- Signature Match API — Veriqos Signature Validation API enables banks, insurance companies, and digital platforms to compare & verify signatures in seconds — checking similarity, flagging mismatches, and automating manual verification.
See how these checks are applied in Banks & NBFCs, Insurance Providers.