Liveness Detection

Tuteliq’s liveness detection combines token-based challenge-response with visual analysis across multiple frames to distinguish real people from spoofing attempts. It runs automatically during identity verification — no additional integration required.

Attack vectors covered

Attack	How it’s caught
Printed photo	Texture analysis detects flat/paper surfaces. No landmark motion across frames.
Photo on screen	Moire pattern detection, screen pixel grid artifacts, unnatural glow and reflections.
Video replay	Cross-frame luminance analysis detects zero natural variation. Background pixel-diffing catches static overlays.
3D printed mask	Depth cue analysis — real selfies have natural depth-of-field (sharp face, slightly blurred background). Masks show uniform sharpness.
Deepfake video	Temporal consistency analysis catches morphing artifacts. Face descriptor comparison across frames flags identity shifts.
Photo cutout overlay	Background thumbnail comparison across frames — identical backgrounds flag static cutout overlays.

How it works

Liveness verification runs two independent layers:

Layer 1: Token-based challenge-response

A cryptographic liveness token is generated for each verification session. The token is HMAC-signed with a server-side secret and includes:

Session timestamp (prevents replay across sessions)
Challenge nonce (prevents pre-recorded responses)

The token must be submitted with the verification request and is validated server-side. Expired or reused tokens are rejected.

Layer 2: Visual liveness analysis

When liveness frames are provided (multiple captures from the verification session), Tuteliq runs four parallel sub-analyzers:

Landmark Motion (35%)

Tracks 7 key facial landmarks (nose tip, chin, brow midpoint, eye corners, mouth corners) across frames. Real people show natural micro-movements. Static photos show zero displacement. Abnormal independent landmark movement flags physiological impossibility.

Texture Analysis (25%)

Crops the face region and applies Laplacian kernel to measure sharpness variance. Real skin has texture (pores, fine lines). Printed photos and screens appear unnaturally smooth or show printer dot patterns. Moire detection at multiple scales catches screen recapture.

Depth Cues (15%)

Compares Laplacian variance between face region and background. Real selfies show natural depth-of-field — sharp face, slightly blurred background. Flat media (screens, prints) show roughly equal sharpness everywhere. A sharpness ratio near 1.0 across all frames is suspicious.

Cross-Frame Consistency (25%)

Compares 128-dimensional face descriptors across all frames (must be same person, distance < 0.55). Compares mean luminance across frames (real = slight natural variation; screen = zero variation). Diffs background thumbnails across frames (identical = cutout overlay).

Combined score

Each sub-analyzer produces a pass/fail result. The combined liveness score is a weighted average:

score = 0.35 * landmark + 0.25 * texture + 0.15 * depth + 0.25 * cross_frame
passed = score >= 0.6

The threshold is configurable per deployment.

Face matching

Face matching runs alongside liveness detection as part of identity verification:

Face detection on both the document photo and the live selfie using 68-point facial landmarks
128-dimensional face descriptor extraction from both faces
Euclidean distance comparison with a configurable similarity threshold (default: 0.6)
Match result includes similarity score for transparency

Face matching confirms the document belongs to the person presenting it. Without liveness, face matching alone can be beaten by holding up someone else’s document — which is why both checks run together.

User experience

The verification UI guides users through a brief liveness check:

Look straight at the camera
Turn head left and right
Blink when prompted
Smile for the camera

These prompts are localized in 10 languages (English, Spanish, Portuguese, Ukrainian, Swedish, Norwegian, Danish, Finnish, German, French) and capture the frames needed for visual liveness analysis.

Response data

When liveness analysis completes, the verification response includes:

{
  "liveness": {
    "valid": true,
    "visual_score": 0.87,
    "visual_checks": {
      "landmark_motion": true,
      "texture": true,
      "depth_cues": true,
      "cross_frame": true
    }
  }
}

Field	Description
`valid`	Overall liveness result
`visual_score`	Combined visual liveness score (0.0-1.0)
`visual_checks.landmark_motion`	Facial landmarks showed natural movement
`visual_checks.texture`	Face texture consistent with real skin
`visual_checks.depth_cues`	Natural depth-of-field detected
`visual_checks.cross_frame`	Consistent identity and natural variation across frames

Getting Started

SDKs

Integrations

Verification

Advanced

Compliance

Liveness Detection

Attack vectors covered

How it works

Layer 1: Token-based challenge-response

Layer 2: Visual liveness analysis

Landmark Motion (35%)

Texture Analysis (25%)

Depth Cues (15%)

Cross-Frame Consistency (25%)

Combined score

Face matching

User experience

Response data

Next steps

Document Checks

Fraud Prevention

​Attack vectors covered

​How it works

​Layer 1: Token-based challenge-response

​Layer 2: Visual liveness analysis

Landmark Motion (35%)

Texture Analysis (25%)

Depth Cues (15%)

Cross-Frame Consistency (25%)

​Combined score

​Face matching

​User experience

​Response data

​Next steps

Document Checks

Fraud Prevention

Attack vectors covered

How it works

Layer 1: Token-based challenge-response

Layer 2: Visual liveness analysis

Combined score

Face matching

User experience

Response data

Next steps