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AI Face Detection

Detect faces in an image and return bounding box coordinates

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

AI Face Detection is an intelligent tool that uses advanced algorithms to identify human faces in images. It returns precise bounding box coordinates for each detected face, enabling automated analysis and integration into various applications.

When to Use

  • •When you need to automatically detect and locate faces in photos for tagging, organization, or editing.
  • •In security or surveillance systems to monitor and identify individuals in images or video frames.
  • •For content moderation or analytics to filter or analyze images based on the presence of faces.

How It Works

  • •Upload an image file in a supported format such as JPEG or PNG.
  • •Optionally adjust parameters like minimum confidence level to filter detections and maximum number of faces to detect.
  • •The AI processes the image and returns a JSON response containing bounding box coordinates for each detected face.

Use Cases

Automated photo tagging in social media or digital asset management systems.
Real-time face detection in security camera feeds for access control or monitoring.
Analyzing customer demographics in retail by detecting faces in store camera images.

Examples

1. Batch Photo Sorting for Event Photography

Photographer
Background
A photographer has hundreds of images from a wedding event and needs to quickly identify photos with faces for editing and delivery.
Problem
Manually reviewing each image to find those containing faces is time-consuming and prone to error.
How to Use
Upload the image files to the tool, set minConfidence to 0.7 to ensure reliable detections, and process them to get face coordinates.
Example Config
minConfidence: 0.7
Outcome
The tool returns JSON with bounding box coordinates, allowing the photographer to automate sorting and focus on editing images with faces.

2. Security System Integration for Access Control

Security Manager
Background
A building's security system captures images at entry points, and the manager needs to detect faces for automated verification.
Problem
Detecting faces in real-time under varying lighting conditions to trigger access alerts.
How to Use
Integrate the tool with the camera feed, upload each frame as an image, set minConfidence to 0.5 for broader detection, and limit maxResults to 5 per frame.
Example Config
minConfidence: 0.5, maxResults: 5
Outcome
Faces are detected with bounding boxes, enabling the system to compare against a database and grant or deny access automatically.

Try with Samples

image, file
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
Hugging Face Deploy Samples
Hugging Face model deployment examples including Transformers, Diffusers, and Gradio applications with Docker and cloud deployment
title token face
image
Windows Image Processing - C# Samples
Comprehensive C# image processing examples for Windows platform including image manipulation, filtering, format conversion, and advanced computer vision operations
preferred input family image
image
OpenCV Computer Vision Samples
OpenCV.js examples for image processing, computer vision, and real-time video analysis in JavaScript
preferred input family image
image

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FAQ

What image formats are supported?

The tool supports common image formats including JPEG, PNG, and GIF, with a maximum file size of 30 MB.

How accurate is the face detection?

Accuracy depends on image quality and settings; increasing the minimum confidence level can improve precision by filtering out low-confidence detections.

Can it detect multiple faces in one image?

Yes, you can set a maximum number of faces to detect or leave it empty to identify all faces present in the image.

What does the output look like?

The output is a JSON object that includes bounding box coordinates (e.g., x, y, width, height) for each detected face.

Is there a limit on the number of faces detected?

You can specify a maximum of up to 100 faces per image; if not set, the tool will detect all faces it can identify.

API Documentation

Request Endpoint

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

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-detection 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-detection": {
      "name": "ai-face-detection",
      "description": "Detect faces in an image and return bounding box coordinates",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=ai-face-detection",
      "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]