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AI Face Align & Crop

Detect, align, and crop faces into individual images (zip when multiple)

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
file
Sample Coverage
4
API Ready
Yes

Overview

This AI-powered tool detects faces in uploaded images, aligns them based on facial landmarks, and crops each face into a separate image. When multiple faces are found, they are automatically packaged into a zip file for convenient download.

When to Use

  • •When you need to extract individual faces from group photos or event pictures.
  • •For preparing aligned face images for machine learning datasets or facial recognition systems.
  • •To create consistent profile pictures or avatars from larger images without manual cropping.

How It Works

  • •Upload an image file containing one or more faces.
  • •The AI model scans the image to detect faces, using an adjustable confidence threshold.
  • •Each detected face is aligned using facial landmark detection for uniform orientation.
  • •Faces are cropped into individual images and saved; if multiple, they are compressed into a zip file.

Use Cases

Batch processing of event photos to extract attendee headshots for identification or records.
Generating training data for AI models by creating a dataset of aligned, cropped face images.
Automating the creation of individual portraits from family or team pictures for social media or profiles.

Examples

1. Extract Faces from a Team Photo

Background
A project manager has a group photo of the team and needs individual headshots for the company directory.
Problem
Manually cropping each face is tedious and often results in misaligned or inconsistent images.
How to Use
Upload the team photo, set minConfidence to 0.6 to capture all faces reliably, and leave maxFaces empty to process everyone.
Example Config
minConfidence: 0.6, maxFaces: (empty)
Outcome
Each team member's face is detected, aligned, and cropped into a separate image, delivered as a zip file for easy download.

2. Prepare Face Dataset for Model Training

Data Scientist
Background
A data scientist is building a facial recognition model and needs a clean set of aligned face images from raw photos.
Problem
Raw images have faces in varying poses and sizes, which can reduce model accuracy if not standardized.
How to Use
Upload a batch of images, set minConfidence to 0.8 for high-quality detections, and limit maxFaces to 1 per image to focus on primary subjects.
Example Config
minConfidence: 0.8, maxFaces: 1
Outcome
Consistently aligned and cropped face images are generated, ready for use in training datasets without additional preprocessing.

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
SVG Samples
Scalable Vector Graphics (SVG) samples demonstrating various SVG features and techniques
preferred input family image
image
Grafana Advanced Application Samples
Comprehensive Grafana samples covering advanced dashboard design, alerting configurations, data source integrations, and plugin development
preferred input family image
image

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FAQ

What image formats are supported?

The tool accepts common image formats like JPEG, PNG, and others as specified in the upload option.

How does the confidence setting affect detection?

The minimum confidence value (0-1) filters detections; higher values reduce false positives but may miss some faces.

Can I limit the number of faces processed?

Yes, use the max faces option to set a limit; leave it empty to detect all faces in the image.

What happens if no faces are detected?

The tool will return an error message or an empty result, prompting you to check the image or adjust settings.

Is the original image quality preserved in the output?

Yes, faces are cropped without compression loss, maintaining the original resolution and quality.

API Documentation

Request Endpoint

POST /en/api/tools/ai-face-align-crop

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-align-crop to get filePath, then pass filePath to the corresponding file field.

Response Format

{
  "filePath": "/public/processing/randomid.ext",
  "fileName": "output.ext",
  "contentType": "application/octet-stream",
  "size": 1024,
  "metadata": {
    "key": "value"
  },
  "error": "Error message (optional)",
  "message": "Notification message (optional)"
}
File: File

AI MCP Documentation

Add this tool to your MCP server configuration: 

{
  "mcpServers": {
    "elysiatools-ai-face-align-crop": {
      "name": "ai-face-align-crop",
      "description": "Detect, align, and crop faces into individual images (zip when multiple)",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=ai-face-align-crop",
      "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]