AI Face Landmarks

AI Face Landmarks | Online Free Tool | Elysia Tools

Tool usage guide

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Category: AI Tools
Input types: file, number
Output type: json
Sample coverage: 4
API ready: Yes

Overview

The AI Face Landmarks tool detects 68 facial keypoints in uploaded images and returns their precise coordinates in JSON format. It enables detailed facial feature analysis for various computer vision applications.

When to use

When you need to extract detailed facial geometry from images for analysis or processing tasks.
For developing or testing facial recognition systems that rely on accurate landmark detection.
In projects involving augmented reality, emotion analysis, or avatar creation where facial keypoints are essential.

How it works

1Upload an image file in a supported format such as JPEG or PNG.
2Optionally adjust the minimum confidence threshold and set the maximum number of faces to detect.
3The AI model processes the image to identify and locate 68 facial landmarks per face.
4Receive a structured JSON response containing the x and y coordinates for each detected keypoint.

Use cases

Building facial recognition systems for security or user authentication applications.
Creating interactive augmented reality filters that respond to facial movements in real-time.
Conducting research on facial expressions by analyzing landmark positions in images or video frames.

Examples

1. Selfie App Facial Alignment

Mobile App Developer

Background

A developer is creating a selfie app that applies virtual accessories based on facial features.

Problem

Need accurate facial landmark coordinates to align accessories like glasses or hats with the user's face.

How to use

Upload a test selfie image and set minConfidence to 0.8 for reliable detection in varied lighting.

{"minConfidence": 0.8, "maxResults": 1}

Outcome

Received JSON with 68 landmark coordinates, enabling precise placement of virtual accessories on the detected face.

2. Emotion Analysis in Customer Feedback

Background

A market research team analyzes facial expressions from video interviews to gauge customer satisfaction.

Problem

Extracting facial landmarks from video frames to compute metrics like smile intensity or brow position.

How to use

Extract key frames from the video, upload each image with default settings for comprehensive detection.

Outcome

Obtained coordinate data for facial points, which was used to quantify emotional responses and improve product insights.

FAQ

What image formats are supported?

The tool accepts common image formats under the image/* type, including JPEG, PNG, and GIF.

How does the minConfidence setting affect results?

A higher minConfidence value (e.g., 0.7) reduces false positives but may miss some faces, while lower values detect more faces with potentially less accuracy.

Can it detect multiple faces in a single image?

Yes, use the maxFaces option to limit detection to a specific number, or leave it empty to detect all faces up to the tool's capacity.

What is the maximum file size allowed?

Image files must be under 30 MB, as specified by the file limit of 31457280 bytes.

What does the output JSON structure look like?

The output is a JSON object with arrays of coordinates for each face, detailing 68 keypoints with x and y values for precise location data.

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Inputs

Result

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Overview

When to use

How it works

Use cases

Examples

1. Selfie App Facial Alignment

2. Emotion Analysis in Customer Feedback

FAQ

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