How to Build a Real-Time Emotion Detector in React Using a Pre-Trained ML Model

In today’s AI-driven world, detecting emotions in real time can greatly enhance user experiences in applications like chatbots, video conferencing tools, or mental health platforms. In this tutorial, we’ll guide you through building a real-time emotion detector using React and a pre-trained machine learning (ML) model.
This project combines React.js for the frontend, TensorFlow.js for in-browser inference, and a pre-trained facial emotion recognition model to classify emotions from webcam input.
Table of Contents
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What Is a Real-Time Emotion Detector?
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Tools and Technologies Required
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Setting Up the React App
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Integrating Webcam Access
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Using a Pre-Trained ML Model with TensorFlow.js
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Performing Real-Time Emotion Detection
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Displaying Results in the UI
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Performance Optimization Tips
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Conclusion
What Is a Real-Time Emotion Detector?
A real-time emotion detector captures facial expressions via a webcam and classifies them into categories like happy, sad, angry, surprised, etc., using machine learning. It’s useful in user feedback systems, live customer service monitoring, and e-learning applications.
Tools and Technologies Required
To build this application, you will need:
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React.js: Frontend JavaScript framework
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TensorFlow.js: ML library to run models in the browser
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Pre-trained emotion detection model: Such as fer2013
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Webcam API: To capture video feed
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HTML5 Canvas: For drawing overlays
Setting Up the React App
First, create your React application:
Install the necessary packages:
Integrating Webcam Access
Use the react-webcam
library to access the webcam:
Using a Pre-Trained ML Model with TensorFlow.js
Download a pre-trained facial emotion recognition model or convert a Keras model to TensorFlow.js format using:
Load the model in your React app:
Performing Real-Time Emotion Detection
Extract frames from the webcam, preprocess them, and make predictions:
Displaying Results in the UI
Update the UI with the detected emotion:
Performance Optimization Tips
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Use
requestAnimationFrame
instead ofsetInterval
for better performance. -
Reduce model size with quantization.
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Use offscreen canvas or Web Workers for image preprocessing.
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Limit prediction frequency to conserve CPU resources.
Conclusion
You’ve now built a basic real-time emotion detection app using React and TensorFlow.js. By leveraging a pre-trained machine learning model, you can classify user emotions directly in the browser—without needing server-side inference. This is a powerful addition to interactive applications across many industries.
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