didit-passive-liveness

name	didit-passive-liveness
description	Integrate Didit Passive Liveness standalone API to verify a user is physically present. Use when the user wants to check liveness, detect spoofing attempts, verify real person presence, implement anti-spoofing, or perform passive liveness detection using Didit. Analyzes a single image without user interaction. Returns liveness score, face quality, and luminance metrics. Detects presentation attacks and duplicate faces.
version	1.2.0
metadata	{"openclaw":{"requires":{"env":"[Truncated]"},"primaryEnv":"DIDIT_API_KEY","emoji":"🧑","homepage":"https://docs.didit.me"}}

Didit Passive Liveness API

Overview

Verifies that a user is physically present by analyzing a single captured image — no explicit movement or interaction required.

Key constraints:

• Supported formats: JPEG, PNG, WebP, TIFF
• Maximum file size: 5MB
• Image must contain exactly one clearly visible face
• Original real-time photo only (no screenshots or printed photos)

Accuracy: 99.9% liveness detection accuracy, <0.1% false acceptance rate (FAR).

Capabilities: Liveness scoring, face quality assessment, luminance analysis, age/gender estimation, spoof detection (screen captures, printed copies, masks, deepfakes), duplicate face detection across sessions, blocklist matching.

Liveness methods: This standalone endpoint uses PASSIVE method (single-frame CNN). Workflow mode also supports ACTIVE_3D (action + flash, highest security) and FLASHING (3D flash, high security).

API Reference: https://docs.didit.me/reference/passive-liveness-api

---

Authentication

All requests require x-api-key header. Get your key from Didit Business Console → API & Webhooks.

---

Endpoint

POST https://verification.didit.me/v3/passive-liveness/

Headers

Header	Value	Required
x-api-key	Your API key	Yes
Content-Type	multipart/form-data	Yes

Request Parameters (multipart/form-data)

Parameter	Type	Required	Default	Constraints	Description
user_image	file	Yes	—	JPEG/PNG/WebP/TIFF, max 5MB	User's face image
face_liveness_score_decline_threshold	integer	No	—	0-100	Scores below this = Declined
rotate_image	boolean	No	—	—	Try rotations to find upright face
save_api_request	boolean	No	true	—	Save in Business Console
vendor_data	string	No	—	—	Your identifier for session tracking

Example

import requests

response = requests.post(
    "https://verification.didit.me/v3/passive-liveness/",
    headers={"x-api-key": "YOUR_API_KEY"},
    files={"user_image": ("selfie.jpg", open("selfie.jpg", "rb"), "image/jpeg")},
    data={"face_liveness_score_decline_threshold": "80"},
)

const formData = new FormData();
formData.append("user_image", selfieFile);
formData.append("face_liveness_score_decline_threshold", "80");

const response = await fetch("https://verification.didit.me/v3/passive-liveness/", {
  method: "POST",
  headers: { "x-api-key": "YOUR_API_KEY" },
  body: formData,
});

Response (200 OK)

{
  "request_id": "a1b2c3d4-...",
  "liveness": {
    "status": "Approved",
    "method": "PASSIVE",
    "score": 95,
    "user_image": {
      "entities": [
        {"age": 22.16, "bbox": [156, 234, 679, 898], "confidence": 0.717, "gender": "male"}
      ],
      "best_angle": 0
    },
    "warnings" 
     
     
  
   

Status Values & Handling

Status	Meaning	Action
"Approved"	User is physically present	Proceed with your flow
"Declined"	Liveness check failed	Check warnings. May be a spoof or poor image quality

Error Responses

Code	Meaning	Action
400	Invalid request	Check file format, size, parameters
401	Invalid API key	Verify x-api-key header
403	Insufficient credits	Top up at business.didit.me

---

Response Field Reference

Field	Type	Description
status	string	"Approved" or "Declined"
method	string	Always "PASSIVE" for this endpoint
score	integer	0-100 liveness confidence (higher = more likely real). null if no face
face_quality	float	0-100 face image quality score. null if no face
face_luminance	float	Face luminance value. null if no face
entities[].age	float	Estimated age
entities[].bbox	array	Face bounding box [x1, y1, x2, y2]
entities[].confidence	float	Face detection confidence (0-1)
entities[].gender	string	"male" or "female"
warnings	array	{risk, log_type, short_description, long_description}

---

Warning Tags

Auto-Decline (always)

Tag	Description
NO_FACE_DETECTED	No face detected in image
LIVENESS_FACE_ATTACK	Potential spoofing attempt (printed photo, screen, mask)
FACE_IN_BLOCKLIST	Face matches a blocklisted entry
POSSIBLE_FACE_IN_BLOCKLIST	Possible blocklist match detected

Configurable (Decline / Review / Approve)

Tag	Description	Notes
LOW_LIVENESS_SCORE	Score below threshold	Configurable review + decline thresholds
DUPLICATED_FACE	Matches another approved session	—
POSSIBLE_DUPLICATED_FACE	May match another user	Configurable similarity threshold
MULTIPLE_FACES_DETECTED	Multiple faces (largest used for scoring)	Passive only
LOW_FACE_QUALITY	Image quality below threshold	Passive only
LOW_FACE_LUMINANCE	Image too dark	Passive only
HIGH_FACE_LUMINANCE	Image too bright/overexposed	Passive only

---

Common Workflows

Basic Liveness Check

1. Capture user selfie
2. POST /v3/passive-liveness/ → {"user_image": selfie}
3. If "Approved" → user is real, proceed
   If "Declined" → check warnings:
     - NO_FACE_DETECTED → ask user to retake with face clearly visible
     - LOW_FACE_QUALITY → ask for better lighting/positioning
     - LIVENESS_FACE_ATTACK → flag as potential fraud

Liveness + Face Match (combined)

1. POST /v3/passive-liveness/ → verify user is real
2. If Approved → POST /v3/face-match/ → compare selfie to ID photo
3. Both Approved → identity verified

---

Utility Scripts

export DIDIT_API_KEY="your_api_key"

python scripts/check_liveness.py selfie.jpg
python scripts/check_liveness.py selfie.jpg --threshold 80