varsharamanujam / emotion_detection_with_cnn

A Convolutional Neural Network (CNN) based solution for emotion detection using the FER2013 dataset. The model is trained to recognize various facial expressions, including anger, disgust, fear, happiness, sadness, surprise.

Emotion_detection_with_CNN

This project aims to detect human emotions using Convolutional Neural Networks (CNN). The emotion detection model is trained on the FER2013 dataset and can recognize seven different emotions: Angry, Disgusted, Fearful, Happy, Neutral, Sad, and Surprised.

Prerequisites

Make sure you have the following packages installed:

pip install numpy
pip install opencv-python
pip install keras
pip3 install --upgrade tensorflow
pip install pillow

Dataset

Download the FER2013 dataset from the link below and place it in the data folder under your project directory:

• https://www.kaggle.com/msambare/fer2013

Training the Emotion Detector

Train the emotion detector with all face expression images in the FER2013 Dataset using the following command:

python TrainEmotionDetector.py

Note: Training may take several hours depending on your processor. (On an i7 processor with 16 GB RAM, it may take around 4 hours.)

After training, the trained model structure and weights will be stored in your project directory as emotion_model.json and emotion_model.h5.

Copy these two files and create a model folder in your project directory, then paste them inside.

Testing the Emotion Detector

Run your emotion detection test file using:

run your emotion detection test file

python TestEmotionDetector.py

This script uses the trained model to perform emotion detection on test images, generating a confusion matrix and classification report.