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

• Python 3
• OpenCV
• Tensorflow
• eel
• tkinter
• matplotlib

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