Emotion-Recognition Web Application With Streamlit

A CNN based Tensorflow implementation on facial expression recognition (FER2013 dataset), achieving 66,72% accuracy

Dependencies:

• python 3.7
  
• Keras with TensorFlow as backend
  
• Streamlit framework for web implementation

FER2013 Dataset:

• Dataset from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
  
• Image Properties: 48 x 48 pixels (2304 bytes)
  
• Labels:

• 0 - Angry 😠
  
• 1 - Disgust 😧
  
• 2 - Fear 😨
  
• 3 - Happy 😃
  
• 4 - Sad 😞
  
• 5 - Surprise 😮
  
• 6 - Neutral 😐
  

• The training set consists of 28,708 examples.
  
• The model is represented as a json file :model.json

The separated dataset is already available to download in the two folders train and test.

Run the project on your local:

• Open Anaconda's command prompt on the project's directory.
  
• Install virtual environment : pip install virtualenv
  
• Create virtual environment : virutalenv venv
  
• Activate the virtual environment : source venv/bin/activate
  
• Install dependencies in requirements.txt : pip install -r requirements.txt
  
• Install Streamlit : pip install streamlit
  
• Run the app.py file : streamlit run app.py"

Demos: