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AI Face Expressions

Classify facial expressions per face (neutral/happy/sad/angry/fearful/disgusted/surprised)

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 Expressions tool uses vision AI to detect faces in uploaded images and classify their expressions into seven categories: neutral, happy, sad, angry, fearful, disgusted, and surprised. It returns structured JSON output for each face, enabling automated emotion analysis.

When to Use

  • •When you need to analyze emotional responses in photos for user research or marketing insights.
  • •For automating content moderation by identifying images with negative expressions like anger or fear.
  • •To enhance interactive applications by detecting user emotions from facial expressions in images.

How It Works

  • •Upload an image file containing one or more faces in supported formats like JPEG or PNG.
  • •The AI model processes the image to detect faces and classify each expression based on visual features.
  • •Optionally adjust parameters such as minimum confidence threshold or maximum number of faces to analyze.
  • •Receive a JSON result with expression classifications and confidence scores for each detected face.

Use Cases

Analyzing customer emotions from photos in feedback surveys to improve product design.
Filtering user-generated content on social platforms to flag images with negative expressions.
Assisting in psychological studies by quantifying facial expressions from participant images.

Examples

1. User Testing Emotion Analysis

UX Designer
Background
A design team captures photos of users interacting with a prototype to assess emotional engagement.
Problem
Manually reviewing and coding emotions from dozens of images is time-consuming and subjective.
How to Use
Upload the image files and set minConfidence to 0.7 to ensure reliable expression detections.
Example Config
{"minConfidence": 0.7}
Outcome
The tool outputs JSON with expression data, helping the team identify pain points and positive reactions efficiently.

2. Social Media Content Screening

Background
An online community platform needs to automatically detect images with fearful or disgusted expressions to maintain a positive environment.
Problem
Manually screening all uploaded images is impractical due to high volume.
How to Use
Process images with maxResults set to 3 to focus on key faces and minConfidence at 0.5 for broader detection.
Example Config
{"maxResults": 3, "minConfidence": 0.5}
Outcome
Images with flagged expressions are queued for review, streamlining moderation efforts.

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 30MB.

How many faces can it detect per image?

It can detect up to 100 faces per image, but you can limit this using the maxFaces option.

What expressions does the tool recognize?

It classifies expressions into seven types: neutral, happy, sad, angry, fearful, disgusted, and surprised.

Can I adjust detection sensitivity?

Yes, use the minConfidence parameter to set a threshold from 0.05 to 0.99, filtering out low-confidence results.

Is the output easy to integrate?

Yes, the result is in JSON format, making it simple to parse and use in applications or data pipelines.

API Documentation

Request Endpoint

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

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-expressions 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-expressions": {
      "name": "ai-face-expressions",
      "description": "Classify facial expressions per face (neutral/happy/sad/angry/fearful/disgusted/surprised)",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=ai-face-expressions",
      "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]