Selfie verification
Selfie verification is the applicant-facing flow that turns a single selfie into a trusted biometric result. Zanyara handles the capture, the quality gate, the liveness check and the face match — so you drop in an SDK and get back a clear outcome, not a pile of raw frames to process yourself.
What the flow does
- Guided capture: on-device framing and quality feedback help the applicant take a usable selfie first time, cutting retries and drop-off.
- Liveness: active challenges plus passive signals confirm a real, present person — see liveness & PAD.
- Face match: the selfie is compared 1:1 to the document portrait or the NFC chip photo — see face match.
- One result: a pass / refer / fail outcome with the underlying signals and reasons for your reviewers and audit trail.
Built to convert
The biggest hidden cost in biometric onboarding is applicants who give up mid-capture. Zanyara’s guided capture, fast quality feedback and cross-device hand-off (start on desktop, finish on a phone) are designed to get a good selfie on the first attempt, so more genuine applicants complete.
Frequently asked questions
- What is the difference between selfie verification and face match?
- Face match is the 1:1 comparison itself. Selfie verification is the whole applicant-facing flow around it: guided capture, quality and framing checks, liveness / presentation-attack detection, and the face match against the document — delivered through a drop-in SDK so the applicant just takes one selfie.
- Which platforms are supported?
- Web, iOS and Android SDKs provide guided selfie capture with on-device framing and quality feedback, plus a cross-device hand-off so a desktop applicant can finish the selfie on their phone. You can also call the API directly if you run your own capture.
- How do you stop a photo or video of someone being used?
- Liveness runs as part of the flow: active challenges (for example head-turn or gesture prompts) plus passive signals confirm a live, present person, and Zanyara defends against screen replays, masks and injected deepfakes. See liveness.