modbender 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": [],
"face_quality": 85.0,
"face_luminance": 50.0
},
"created_at": "2025-05-01T13:11:07.977806Z"
}
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