didit-face-search

name	didit-face-search
description	Integrate Didit Face Search standalone API to perform 1:N facial search against all previously verified sessions. Use when the user wants to detect duplicate accounts, search for matching faces, check if a face already exists in the system, prevent duplicate registrations, search against blocklist, or implement facial deduplication using Didit. Returns ranked matches with similarity percentages.
version	1.1.0
metadata	{"openclaw":{"requires":{"env":["DIDIT_API_KEY"]},"primaryEnv":"DIDIT_API_KEY","emoji":"🔍","homepage":"https://docs.didit.me"}}

Didit Face Search API (1:N)

Overview

Compares a reference face against all previously approved verification sessions to detect duplicate accounts and blocklisted faces. Returns ranked matches with similarity scores.

Key constraints:

• Supported formats: JPEG, PNG, WebP, TIFF
• Maximum file size: 5MB
• Compares against all approved sessions in your application
• Blocklist matches cause automatic decline

Similarity score guidance:

Range	Interpretation
90%+	Strong likelihood of same person
70-89%	Possible match, may need manual review
Below 70%	Likely different individuals

API Reference: https://docs.didit.me/standalone-apis/face-search Feature Guide: https://docs.didit.me/core-technology/face-search/overview

---

Authentication

All requests require x-api-key header. Get your key from Didit Business Console → API & Webhooks, or via programmatic registration (see below).

Getting Started (No Account Yet?)

If you don't have a Didit API key, create one in 2 API calls:

1. Register: POST https://apx.didit.me/auth/v2/programmatic/register/ with {"email": "[e-mail]", "password": "MyStr0ng!Pass"}
2. Check email for a 6-character OTP code
3. Verify: POST https://apx.didit.me/auth/v2/programmatic/verify-email/ with {"email": "[e-mail]", "code": "A3K9F2"} → response includes api_key

To add credits: GET /v3/billing/balance/ to check, POST /v3/billing/top-up/ with {"amount_in_dollars": 50} for a Stripe checkout link.

See the didit-verification-management skill for full platform management (workflows, sessions, users, billing).

---

Endpoint

POST https://verification.didit.me/v3/face-search/

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	Description
user_image	file	Yes	—	Face image to search (JPEG/PNG/WebP/TIFF, max 5MB)
rotate_image	boolean	No	false	Try 0/90/180/270 rotations for non-upright faces
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/face-search/",
    headers={"x-api-key": "YOUR_API_KEY"},
    files={"user_image": ("photo.jpg", open("photo.jpg", "rb"), "image/jpeg")},
)
print(response.json())

const formData = new FormData();
formData.append("user_image", photoFile);

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

Response (200 OK)

{
  "request_id": "a1b2c3d4-...",
  "face_search": {
    "status": "Approved",
    "total_matches": 1,
    "matches": [
      {
        "session_id": "uuid-...",
        "session_number": 1234,
        "similarity_percentage": 95.2,
        "vendor_data": "user-456",
        "verification_date": "2025-06-10T10:30:00Z",
        "user_details": {
          "name": "Elena Martinez",
          "document_type": "Identity Card",
          "document_number": "***456"
        },
        "match_image_url": "https://example.com/match.jpg",
        "status": "Approved",
        "is_blocklisted": false
      }
    ],
    "user_image": {
      "entities": [
        {"age": "27.6", "bbox": [40, 40, 120, 120], "confidence": 0.95, "gender": "female"}
      ],
      "best_angle": 0
    },
    "warnings": []
  }
}

Status Values & Handling

Status	Meaning	Action
"Approved"	No concerning matches found	Proceed — new unique user
"In Review"	Matches above similarity threshold	Review matches[] for potential duplicates
"Declined"	Blocklist match or policy violation	Check matches[].is_blocklisted and warnings

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

Match Object

Field	Type	Description
session_id	string	UUID of the matching session
session_number	integer	Session number
similarity_percentage	float	0-100 similarity score
vendor_data	string	Your reference from the matching session
verification_date	string	ISO 8601 timestamp
user_details.name	string	Name from the matching session
user_details.document_type	string	Document type used
user_details.document_number	string	Partially masked document number
match_image_url	string	Temporary URL (expires 60 min)
status	string	Status of the matching session
is_blocklisted	boolean	Whether the match is from the blocklist

User Image Object

Field	Type	Description
entities[].age	string	Estimated age
entities[].bbox	array	Face bounding box [x1, y1, x2, y2]
entities[].confidence	float	Detection confidence (0-1)
entities[].gender	string	"male" or "female"
best_angle	integer	Rotation applied (0, 90, 180, 270)

---

Warning Tags

Auto-Decline

Tag	Description
NO_FACE_DETECTED	No face found in image
FACE_IN_BLOCKLIST	Face matches a blocklisted entry

Configurable

Tag	Description
MULTIPLE_FACES_DETECTED	Multiple faces detected — unclear which to use

Similarity threshold and allow multiple faces settings are configurable in Console.

Warning severity: error (→ Declined), warning (→ In Review), information (no effect).

---

Common Workflows

Duplicate Account Detection

1. During new user registration
2. POST /v3/face-search/ → {"user_image": selfie}
3. If total_matches == 0 → new unique user
   If matches found → check similarity_percentage:
     90%+ → likely duplicate, investigate matches[].vendor_data
     70-89% → possible match, flag for manual review

Combined Verification + Dedup

1. POST /v3/passive-liveness/ → verify user is real
2. POST /v3/face-search/ → check for existing accounts
3. POST /v3/id-verification/ → verify identity document
4. POST /v3/face-match/ → compare selfie to document photo
5. All Approved → verified, unique, real user

Security: Match image URLs expire after 60 minutes. Store only session_id and similarity_percentage — minimize biometric data on your servers.

---

Utility Scripts

search_faces.py: Search for matching faces from the command line.

# Requires: pip install requests
export DIDIT_API_KEY="your_api_key"
python scripts/search_faces.py selfie.jpg
python scripts/search_faces.py photo.png --rotate --vendor-data user-123