Facial-Expression-Recognition

Project on Facial Expression Recognition with Keras

Link for Dataset: https://drive.google.com/open?id=1adZCwxr8Th7CfMMGytCTvO_LMicGfneH
This Project is based on kaggle competition on Facial expression recognition. Here is asummarized report on task completed in this project.

Task 1: Dataset

• Dataset is also from the same competition.

Task 2: Exploring the Dataset

• Display some images from every expression type in the Emotion FER dataset.
• Check for class imbalance problems in the training data.

Task 3: Generated Training and Validation Batches

• Generate batches of tensor image data with real-time data augmentation.
• Specify paths to training and validation image directories and generates batches of augmented data.

Task 4: Created a Convolutional Neural Network (CNN) Model

• Design a convolutional neural network with 4 convolution layers and 2 fully connected layers to predict 7 types of facial expressions.
• Use Adam as the optimizer, categorical crossentropy as the loss function, and accuracy as the evaluation metric.

Task 5: Trained and Evaluated Model

• Train the CNN by invoking the model.fit() method.
• Use ModelCheckpoint() to save the weights associated with the higher validation accuracy.
• Observe live training loss and accuracy plots in Jupyter Notebook for Keras.

Task 6: Saved and Serialized Model as JSON String

• Sometimes, you are only interested in the architecture of the model, and you don't need to save the weight values or the optimizer.
• Use to_json(), which uses a JSON string, to store the model architecture.

Task 7: Created a Flask App to Serve Predictions

• Use open-source code from "Video Streaming with Flask Example" to create a flask app to serve the model's prediction images directly to a web interface.

Task 8: Created a Class to Output Model Predictions

• Create a FacialExpressionModel class to load the model from the JSON file, load the trained weights into the model, and predict facial expressions.

Task 9: Designed an HTML Template for the Flask App

• Design a basic template in HTML to create the layout for the Flask app.

Task 10: Used Model to Recognize Facial Expressions in Videos

• Run the main.py script to create the Flask app and serve the model's predictions to a web interface.
• Apply the model to saved videos on disk.