Real-Time Face Emotion Recognition using Python, OpenCV, and CNN

#pyresearch

In this video tutorial, you will learn how to implement Face Emotion Recognition using Python, OpenCV, and a Convolutional Neural Network (CNN). We will walk you through the process of collecting a dataset of facial images with labeled emotions, preprocessing the data, training a CNN model, and evaluating its performance. Finally, we will show you how to use the trained model to predict emotions in real-time using OpenCV to capture and process images from a webcam or video stream.

This tutorial is perfect for anyone interested in computer vision, machine learning, and artificial intelligence, and has some basic knowledge of Python programming. By the end of this tutorial, you will have a better understanding of how to use deep learning techniques to detect emotions in real time using Python and OpenCV.

Here's what you'll learn:

How to collect and preprocess a dataset of facial images
How to build and train a CNN model using Keras
How to evaluate the performance of the model on a testing set
How to use OpenCV to capture and process images from a webcam or video stream
How to predict emotions in real-time using the trained model
We will be using the FER2013 dataset for this tutorial, which is available for download on Kaggle. Make sure to follow along with the code in the video and feel free to ask any questions in the comments section.

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[sachinkerala]

Is it possible to caputure/export the emotions with with timestamp in a CSV ? using this model

[wheresthecode]

what is the maximum distance that it will detect a face and do emotion detection? Example yolo can see a person even from 30m. How far can this see a face?