Facial-Emotion-Recognition-using-ML-Python

Codes zip-file download - https://drive.google.com/file/d/1aoa2G3JyCk5-Gty7e8S1gxS4kRpMToP1/view?usp=drive_link

This project demonstrates how to use Convolutional Neural Networks (CNNs) for emotion detection from facial expressions.

Requirements Before you begin, make sure you have the following Python packages installed:

Packages need to be installed

pip install numpy

pip install opencv-python

pip install keras

pip3 install --upgrade tensorflow

pip install pillow

Download FER2013 dataset

You need to download the FER2013 dataset to train the model. You can get it from this link on Kaggle-- https://www.kaggle.com/msambare/fer2013

After downloading, place the dataset in a data folder within your project directory.

Train Emotion detector

To train the emotion detection model:

Open the trainmodel-checkpoint.ipynb file in a Jupyter environment. Run the notebook to train the model using the FER2013 dataset. Note: The training process may take several hours, depending on your hardware. On an i5 processor with 16GB of RAM, it took approximately 4 hours.

After the training is complete, the model structure and weights will be saved as:

emotiondetector.json emotiondetector.keras

Testing

Run your emotion detection test file

python trialvideotester.py

Face Emotion Recognition Using Machine Learning

This project is a practical implementation of facial emotion recognition using machine learning and Python.

You can watch a related tutorial here: https://www.youtube.com/watch?v=aoCIoumbWQY

###Results: