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face-recognition-mcp-server

face-recognition-mcp-server

Local
github.com/CvartelSecurity0Updated 28w ago

About face-recognition-mcp-server

face-recognition-mcp-server wraps the 3DiVi Face SDK for developers building face-recognition workflows with AI coding agents. It processes data locally and exposes tools for detection, liveness, verification, search, and related assessments.

Key features

  1. 01Detects faces and landmarks
  2. 02Checks liveness and image quality
  3. 03Extracts and verifies face templates
  4. 04Searches a face gallery
  5. 05Estimates attributes and emotion
  6. 06Detects face masks

Use cases

  1. 01Building KYC verification workflows
  2. 02Checking liveness during identity checks
  3. 03Matching a face against an enrolled gallery

MCP support

Exposes 3DiVi Face SDK processing functions as callable MCP tools, including face detection, liveness checks, verification, and gallery search. Processing runs on the local machine.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

3DiVi Face SDK — MCP Server

The face recognition MCP server for AI-native development.
On-premise · GDPR-compliant · ISO 30107-3 liveness · No cloud dependency.

License: MIT SDK Version MCP


What this is

A local Model Context Protocol server that wraps the 3DiVi Face SDK Processing Block API as callable tools. AI coding agents (Claude Code, Cursor, Windsurf, VS Code Copilot) can detect faces, verify identity, run liveness checks, and search a face gallery — without writing a single line of SDK integration code.

All processing runs on the developer's machine. No data leaves the host.


Tools exposed

ToolProcessing BlockOutput
detect_facesFACE_DETECTORBounding boxes, landmarks, pitch/yaw/roll
check_livenessLIVENESS_ESTIMATORLiveness verdict + confidence
assess_qualityQUALITY_ASSESSMENT_ESTIMATORQuality score, eyes-open flag
extract_templateFACE_TEMPLATE_EXTRACTORContextTemplate (binary, no PII)
verify_faceVERIFICATION_MODULE1:1 similarity score + match verdict
search_faceMATCHER_MODULE + DynamicTemplateIndex1:N ranked match list
estimate_attributesAGE_ESTIMATOR + GENDER_ESTIMATORAge, gender, confidence
estimate_emotionEMOTION_ESTIMATOR7-class emotion probabilities
detect_maskMASK_ESTIMATORMasked/unmasked verdict

Quick start

Prerequisites
Install
git clone https://github.com/3divi/face-recognition-mcp-server.git
cd face-recognition-mcp-server
pip install -r server/python/requirements.txt
Configure Claude Code

Add to your claude_desktop_config.json (or equivalent for your agent):

{
  "mcpServers": {
    "3divi-face-sdk": {
      "command": "python",
      "args": ["/path/to/face-recognition-mcp-server/server/python/main.py"],
      "env": {
        "FACESDK_PATH": "/path/to/facesdk",
        "FACESDK_CONFIG": "/path/to/facesdk/conf/facerec.conf"
      }
    }
  }
}
Try it

Open Claude Code in any project and type:

Integrate 3DiVi Face SDK for KYC verification — detect the face, run liveness,
extract a template, and compare against an enrolled template.

Claude Code will call the MCP tools directly. No SDK integration code required.


Project structure

face-recognition-mcp-server/
├── CLAUDE.md                        # Machine-readable API reference (read by AI agents)
├── README.md
├── LICENSE
├── server/
│   ├── python/                      # Phase 1 — stdio MCP server
│   │   ├── main.py                  # Entry point (JSON-RPC 2.0 over stdio)
│   │   ├── tools/
│   │   │   ├── detection.py         # detect_faces, check_liveness, assess_quality, detect_mask
│   │   │   ├── recognition.py       # extract_template, verify_face, search_face
│   │   │   └── attributes.py        # estimate_attributes, estimate_emotion
│   │   └── requirements.txt
│   └── nodejs/                      # Phase 2 — coming soon
├── reference-apps/
│   ├── kyc-onboarding/              # Phase 2
│   ├── access-control/              # Phase 2
│   └── emotion-analytics/           # Phase 3
└── docs/
    └── threshold-guide.md

Why on-premise matters

Privacy regulations (GDPR, BIPA, India DPDP) prohibit sending biometric data to third-party cloud APIs in many regulated industries. This MCP server runs entirely local — the SDK shared libraries process images in-process, and no biometric data is transmitted anywhere.

The only competing biometric MCP server in the registry is Microsoft Azure's liveness wrapper, which requires Azure cloud. This server requires nothing beyond the SDK licence you already have.


Similarity threshold guide

See docs/threshold-guide.md for use-case-specific recommendations.

Use caseRecommended threshold
Physical access control0.97
Enterprise time/attendance0.95
KYC / onboarding0.93
Visitor watchlist0.90
Retail re-identification0.85

Licence

MIT — the MCP server is open source. The underlying 3DiVi Face SDK requires a separate licence from 3divi.ai.


Links

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.