Face AI

Tatvora Face Liveness AI

Verify that the person in front of the camera is real and live.

Tatvora Face Liveness AI protects face-based workflows from presentation attacks. It evaluates whether the subject is physically present at capture time instead of a printed photograph, a screen replay or another spoofing attempt. Pairing liveness with verification is what turns a face check into an authentication control rather than a photograph comparison.

POST https://api.tatvora.com/api/v1/face/liveness
POST https://api.tatvora.com/api/v1/face/liveness
curl -X POST https://api.tatvora.comPOST /api/v1/face/liveness \
  -H "Authorization: Bearer $TATVORA_API_KEY" \
  -F "image=@selfie.jpg"
What this model does — and does not — do Liveness detection reduces the risk of presentation attacks; it is one control within a security design rather than a guarantee. Tatvora does not publish accuracy figures for this model. Request current performance characteristics for your capture conditions during evaluation.
How it works

The Face Liveness pipeline

  1. Camera capture
  2. Tatvora Face Liveness AI
  3. is_live / spoof_detected
  4. Allow or block
See it work

What you send, and what you get back

A worked example of a single Face Liveness AI call, end to end.

POST /api/v1/face/liveness

Case 1 · Real person at the camera

LIVE
is_live true
spoof_detected false

Case 2 · Photo held up to the camera

SPOOF DETECTED
is_live false
spoof_detected true

Face Verification asks

“Does this face match the reference face?”

Face Liveness asks

“Is the person actually live in front of the camera?”

{
  "is_live": true,
  "spoof_detected": false
}

Sample data for illustration. Liveness raises the cost of a presentation attack — printed photos, screen replays and similar — but no liveness model is absolute. Treat it as one control within a wider security design. We publish no accuracy figures; request current performance characteristics for your capture conditions during evaluation.

Capabilities

What Face Liveness AI can do

  • Liveness detection at the point of capture
  • Live person validation
  • Printed photo attack detection
  • Screen and video replay detection
  • Spoof and fake face detection
  • Camera-based validation for web and mobile capture
  • Authentication protection when combined with verification
Use cases

Where it is applied

  • Employee attendance that must resist photo spoofing
  • Banking and financial authentication
  • Login and step-up authentication
  • Digital onboarding
  • Remote identity verification
  • KYC workflows
  • Account security controls
  • Workforce management
API reference

Request and response

Authenticate with a bearer API key over HTTPS. Upload a file as multipart/form-data and receive a JSON response.

Request fields

  • image — file part, multipart/form-data
  • Authorization: Bearer YOUR_API_KEY — required header

Endpoints and payloads shown on this site are illustrative examples of the published interface. Never expose a secret API key in client-side code.

200 OK — example response
{
  "success": true,
  "request_id": "req_e4419c07",
  "is_live": true,
  "spoof_detected": false
}
FAQ

Face Liveness AI questions

Face Liveness determines whether the person in front of the camera is physically present at capture time, rather than a printed photo or a screen replay. It is usually combined with verification or recognition to turn a face check into an authentication control.

Detecting presentation attacks — printed photographs, screen and video replays, and similar spoofing attempts — is what the model is built for. It meaningfully raises the cost of an attack, but no liveness model is absolute, so treat it as one control in a wider security design. We do not publish accuracy figures; request current performance characteristics for your capture conditions during evaluation.
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Try Tatvora Face Liveness AI

Request an evaluation key and validate the model against your own data before choosing a plan.

Questions? Email support@tatvora.com