Real-Time Face Tracking with AI and Augmented Reality (AR)

In this lesson, we will use MindAR and A-Frame to track faces in real time and experience virtual try-ons with Augmented Reality (AR).

Since this lesson utilizes the camera of a smart device (e.g., a laptop webcam or a smartphone front camera) to recognize faces, you need to enable the necessary camera permissions for the lesson to proceed.

If you are using a desktop without a webcam, you may have difficulty following along. We recommend using a tablet or laptop instead.

The MindAR library used in this lesson is a web-based Augmented Reality (AR) library that enables AR experiences directly in the browser using JavaScript and WebAR technology.

For more details, please refer to the official MindAR documentation.

Now, let’s explore how AI detects faces in real time and applies AR effects. :)

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The system uses AI-powered facial landmark detection provided by MindAR.

Landmarks refer to key points on the face, such as the tip of the nose or the centers of both eyes. MindAR leverages a lightweight machine learning model to detect facial landmarks, including the eyes, nose, and mouth, using webcam input.

These landmarks act as anchor points, allowing AR objects to stay aligned with the user's face in real time, even as they move.

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Once facial landmarks are detected, 3D models are aligned to these anchors.

Even as the user moves their face, the 3D model remains positioned naturally by following these anchor points.

To render AR content, we use A-Frame.

A-Frame is an open-source framework based on WebVR (WebXR) and WebGL, making it easy to create 3D content using HTML.

MindAR integrates with A-Frame to implement both marker-based AR and face-tracking AR directly in HTML.

For more information about A-Frame, please visit the official documentation.