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Emotion detection using deep learning

Introduction

This project aims to classify the emotion on a person's face into one of seven categories:

  • angry
  • disgusted
  • fearful
  • happy
  • neutral
  • sad
  • surprised

The project uses deep convolutional neural networks. The model is trained on the FER-2013 dataset, The dataset consists of 35887 grayscale, 48x48 sized face images.

The User interface is built using eel, a Python library that allows to create web applications in Python.

Dependencies

Download the FER-2013 dataset from here
  • To run the project (windows)
python main.py
  • To run the project (linux/unix)
python3 main.py

Algorithm

  • First, the haar cascade method is used to detect faces in each frame of the webcam feed.

  • The region of image containing the face is resized to 48x48 and is passed as input to the CNN.

  • The network outputs a list of softmax scores for the seven classes of emotions.

  • The emotion with maximum score is displayed on the screen.

  • Algorithm Code here