Face Recognition Mcp Server

io.github.student920Published by GitHub account student920v1.0.0Updated Oct 24, 2026

On-premise face recognition MCP server for 3DiVi Face SDK. No cloud required.

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Server details

Identifier: io.github.student920/face-recognition-mcp-server
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Sep 16, 2026

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Overview

AI-generated overview

A local MCP server that exposes 3DiVi Face SDK tools so an assistant can detect faces, verify identity, check liveness, and search a face gallery.

What you can do: Wraps the 3DiVi Face SDK Processing Block API as callable tools. Exposed tools include detect_faces for bounding boxes, landmarks and head pose; check_liveness; assess_quality; extract_template; verify_face for 1:1 comparison; search_face for 1:N gallery matching; estimate_attributes for age and gender; estimate_emotion; and detect_mask. All processing runs in-process on the developer's machine.
When to use it: Useful when building or prototyping biometric features such as KYC onboarding, access control, time and attendance, or watchlist matching, and you want an AI coding agent to call face SDK operations directly instead of writing SDK integration code. It suits regulated settings where biometric data must stay on the host.
Requirements: Runs as a local stdio process on a desktop machine. Needs 3DiVi Face SDK 3.29 installed, Python 3.9 or later, and a valid Face SDK licence. Configuration uses the environment variables FACESDK_PATH and FACESDK_CONFIG. No cloud service or network access is required.

Before you install

It processes biometric data, including face images, templates, age, gender and emotion estimates, so consent, retention and local privacy rules apply. The README states no biometric data leaves the host, but that depends on the SDK and configuration you supply. A Face SDK licence is required, and the README does not state its cost or terms.

Installation

In SourceWeft

Open Face Recognition Mcp Server in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

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

Tool	Processing Block	Output
`detect_faces`	`FACE_DETECTOR`	Bounding boxes, landmarks, pitch/yaw/roll
`check_liveness`	`LIVENESS_ESTIMATOR`	Liveness verdict + confidence
`assess_quality`	`QUALITY_ASSESSMENT_ESTIMATOR`	Quality score, eyes-open flag
`extract_template`	`FACE_TEMPLATE_EXTRACTOR`	ContextTemplate (binary, no PII)
`verify_face`	`VERIFICATION_MODULE`	1:1 similarity score + match verdict
`search_face`	`MATCHER_MODULE` + `DynamicTemplateIndex`	1:N ranked match list
`estimate_attributes`	`AGE_ESTIMATOR` + `GENDER_ESTIMATOR`	Age, gender, confidence
`estimate_emotion`	`EMOTION_ESTIMATOR`	7-class emotion probabilities
`detect_mask`	`MASK_ESTIMATOR`	Masked/unmasked verdict

Quick start

Prerequisites

3DiVi Face SDK 3.29 installed on your machine
Python 3.9+
A valid Face SDK licence

Install

bash

git clone https://github.com/3divi/face-recognition-mcp-server.gitcd face-recognition-mcp-serverpip install -r server/python/requirements.txt

git clone https://github.com/3divi/face-recognition-mcp-server.gitcd face-recognition-mcp-serverpip install -r server/python/requirements.txt

Configure Claude Code

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

json

{  "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"      }    }  }}

{  "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.

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

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.

Licence

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

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v1.0.0LatestSep 16, 2026

Score

50% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

Reached in the last 30 days25 pts
Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.
Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
DescriptionRequired15 pts
Logo10 pts
Documentation15 pts
License10 pts

Use case	Recommended threshold
Physical access control	0.97
Enterprise time/attendance	0.95
KYC / onboarding	0.93
Visitor watchlist	0.90
Retail re-identification	0.85