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AI Face Descriptors

Generate 128D face embeddings for each detected face

Click to upload file or drag and drop file here

Maximum file size: 30MB Supported formats: image/*

Key Facts

Category
AI Tools
Input Types
file, number
Output Type
json
Sample Coverage
4
API Ready
Yes

Overview

The AI Face Descriptors tool generates 128-dimensional face embeddings for each detected face in an uploaded image. These embeddings are numerical representations of facial features, ideal for face recognition, verification, and analysis tasks.

When to Use

  • •When building or testing facial recognition systems for security or authentication.
  • •For extracting facial features from images to create databases for verification purposes.
  • •In research or development projects involving computer vision and face analysis.

How It Works

  • •Upload an image file containing one or more faces.
  • •Optionally adjust parameters like minimum confidence threshold and maximum number of faces to detect.
  • •The tool processes the image and returns a JSON response with 128D embeddings for each detected face.

Use Cases

Integrating into security systems for facial authentication and access control.
Organizing photo libraries by automatically identifying and grouping faces based on embeddings.
Supporting academic research in biometrics and computer vision for face analysis.

Examples

1. Secure Employee Login

IT Security Specialist
Background
A company wants to implement facial recognition for employee login to secure systems.
Problem
Need to generate accurate face embeddings from employee ID photos to create a verification database.
How to Use
Upload employee photos, set minConfidence to 0.8 for high accuracy, and process each image to extract embeddings.
Outcome
Reliable face embeddings are obtained and stored for real-time verification during login attempts.

2. Photo Event Tagging

Event Photographer
Background
After a conference, hundreds of photos need to be sorted by attendee for quick sharing.
Problem
Manually tagging faces in each photo is time-consuming and prone to errors.
How to Use
Batch upload all event photos, use default settings to detect faces, and extract embeddings for clustering.
Outcome
Faces are grouped based on similar embeddings, enabling efficient tagging and search across the photo collection.

Try with Samples

image, file
Hugging Face Deploy Samples
Hugging Face model deployment examples including Transformers, Diffusers, and Gradio applications with Docker and cloud deployment
title token face
image
Hugging Face JavaScript Samples
Hugging Face JavaScript/TypeScript examples using Transformers.js for NLP, computer vision, and multimodal tasks in browser and Node.js
title token face
image
PDF Samples
Generated PDF samples from tools dated 2026-02-01 to 2026-02-10
preferred input family image
image
SVG Samples
Scalable Vector Graphics (SVG) samples demonstrating various SVG features and techniques
preferred input family image
image

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FAQ

What is a face embedding?

A face embedding is a 128-dimensional vector that numerically represents facial features, used for comparison in recognition tasks.

What image formats are supported?

The tool supports common image formats such as JPEG, PNG, and others as specified in the upload interface.

How does the minimum confidence parameter work?

Set minConfidence between 0.05 and 0.99 to filter detections; higher values reduce false positives by requiring more certainty.

Can it handle images with multiple faces?

Yes, it detects all faces by default, or you can set maxResults to limit the number of faces processed.

What is the output format?

The output is a JSON array containing objects for each detected face, including the 128D embedding and detection metadata.

API Documentation

Request Endpoint

POST /en/api/tools/ai-face-descriptors

Request Parameters

Parameter Name Type Required Description
imageFile file (Upload required) Yes -
minConfidence number No -
maxResults number No -

File type parameters need to be uploaded first via POST /upload/ai-face-descriptors to get filePath, then pass filePath to the corresponding file field.

Response Format

{
  "key": {...},
  "metadata": {
    "key": "value"
  },
  "error": "Error message (optional)",
  "message": "Notification message (optional)"
}
JSON Data: JSON Data

AI MCP Documentation

Add this tool to your MCP server configuration: 

{
  "mcpServers": {
    "elysiatools-ai-face-descriptors": {
      "name": "ai-face-descriptors",
      "description": "Generate 128D face embeddings for each detected face",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=ai-face-descriptors",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

You can chain multiple tools, e.g.: `https://elysiatools.com/mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp`, max 20 tools.

Supports URL file links or Base64 encoding for file parameters.

If you encounter any issues, please contact us at [email protected]