Real Time AI Face Landmark Detection in 20 Minutes with Tensorflow.JS and React

Introduction

Have you ever wondered how Snapchat filters can seamlessly pick up your face? The secret lies in facial landmark detection—an incredible technique that utilizes machine learning, deep learning, and AI to identify various points on your face. In this article, we'll explore how you can create your very own facial landmark detection app using Tensorflow.js and React.

What is Facial Landmark Detection?

Facial landmark detection is an advanced method that detects specific points on the surface of a face, such as the eyes, nose, and mouth. By connecting these points, you can create a detailed 3D mesh of a face. The model we will use for our application detects a total of 486 3D facial landmarks, making it highly accurate and capable of constructing a detailed representation of facial geometry.

Overview of What We Will Cover

In this article, we will go through three key components to build our facial landmark detection app:

1. Installing Face Mesh: A pre-trained deep learning library from the Tensorflow.js team that allows us to perform facial landmark recognition.
2. Building a React App: Creating a simple React application that utilizes the Face Mesh model.
3. Real-Time Detection: Using the webcam to detect faces and visualize landmarks in real-time.

Getting Started

We will primarily work with React and Tensorflow.js for this project. React is a popular front-end JavaScript library that helps us build applications quickly. To begin, we'll create a new React app using the command:

npx create-react-app face-mesh

After creating the app, navigate into the newly created folder and open it in your preferred code editor.

Installing Dependencies

The three key dependencies we need are:

• Tensorflow.js
• The Face Mesh model from Tensorflow.js
• React Webcam for accessing your computer's webcam

You can install these dependencies by using the following command:

npm install @tensorflow/tfjs @tensorflow-models/face-mesh react-webcam

After installing the dependencies, run the app to make sure everything is set up correctly:

npm run start

Importing Dependencies

After confirming the app is running, we proceed to import the required dependencies into the App.js file. You'll need to set up references for both the webcam and canvas component.

Setting Up Webcam and Canvas

Set up your webcam and canvas using React Webcam and HTML Canvas. The webcam will act as the input source for our face mesh detection.

Loading Face Mesh

Define an asynchronous function called runFaceMesh to load the Face Mesh model. You can set input resolution and scale for better performance:

const runFaceMesh = async () => (
  const net = await faceMesh.load({
    inputResolution: { width: 640, height: 480 ),
    scale: 0.8,
  });
};

Detection Function

To detect faces, check if the webcam is active and receiving data. Then set properties for the webcam and canvas and run the detection logic:

const detect = async (net) => (
  const video = webcamRef.current.video;
  // Detection logic...
);

You’ll need to create an interval to run the detection every 100 milliseconds.

Drawing Utilities

For visualization, we create a separate utilities.js file that contains functions for drawing points and triangles based on detected landmarks. The triangulation metrics are essential for accurately drawing triangles on the face.

Finalizing the App

Once the drawing utilities are set up, you will integrate them into your detection function to visualize the detected facial landmarks in real time.

Lastly, test your application. If everything is functioning correctly, you will see a solid representation of your facial landmarks directly on your face, just like a Snapchat filter!

Conclusion

You have successfully built a real-time facial landmark detection app using Tensorflow.js and React. This application provides the foundation for creating your own Snapchat-style filters and exploring the incredible capabilities of AI in real time.

Keywords

• Facial Landmark Detection
• Tensorflow.js
• React
• Face Mesh
• Real-Time Detection
• Webcam
• Triangulation

FAQ

Q: What is facial landmark detection?

A: Facial landmark detection is a technique that uses AI to identify key points on a face, enabling the creation of a detailed 3D mesh representation.

Q: What tools do I need to create a facial landmark detection app?

A: You will need Node.js, React, Tensorflow.js, and the React Webcam library.

Q: How accurate is the model used in this app?

A: The model can detect 486 3D facial landmarks, allowing for highly accurate representation of facial geometry.

Q: Can this technology be used for creating filters similar to Snapchat?

A: Yes! This implementation provides a solid foundation for building Snapchat-style filters and other facial recognition applications.