Facial Emotions Recognition through Deep learning

We will create a real-time facial emotions recognition system using python. This system can be tested on both videos including webcam and photos.

In this project, we will create a real-time facial emotions recognition system using python. This system can be tested on both videos including webcam and photos.

Here’s a demo video of the project before we dive into the code:

Dataset

FER2013 dataset was used for this project. FER2013 is an open-source facial emotions recognition dataset containing approximately 30,000 grayscale, resized images 48×48 pixel images. The goal is to put each face into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral) depending on the emotion expressed in the facial expression. There are 28,709 instances in the training set and 3,589 examples in the public test set.

Import Libraries and Dependencies

Tensorflow library was used for training the machine learning model along with Keras for developing a neural network architecture.

Loading the dataset

For this project, the training was performed in Google Colab. Hence the training and testing dataset were unzipped in the colab.

Mounting Google Drive:

Installing unrar so that it can be used to retrieve data. Note that you need to keep the zipped folders of dataset in your drive

Exploring Dataset

Preparing Dataset for Training

Model Architecture

Model Training

Evaluating Model Performance

PLOTTING TRAINING LOSS/ VAL-LOSS VS EPOCHS

PLOTTING ACCURACY/VAL_ACCURACY VS EPOCHS

Saving Model

Saving the trained model is important so that it can be loaded later for testing without the need of training all over again

Test dataset accuracy

Plotting predictions

Real time Testing (Webcam/Videos)

Testing part is executed on Jupyter Notebook and not on Google Colab. Make sure to provide the path for trained model which we saved after the training. Drawing a rectangle over the face is an optional part. To run that part make sure you provide the path of frontal face haarcascade xml file

Originally published at https://visione.website on June 9, 2022.