  Facial expression recognition is a topic of great interest in most fields
from artificial intelligence and gaming to marketing and healthcare. The goal
of this paper is to classify images of human faces into one of seven basic
emotions. A number of different models were experimented with, including
decision trees and neural networks before arriving at a final Convolutional
Neural Network (CNN) model. CNNs work better for image recognition tasks since
they are able to capture spacial features of the inputs due to their large
number of filters. The proposed model consists of six convolutional layers, two
max pooling layers and two fully connected layers. Upon tuning of the various
hyperparameters, this model achieved a final accuracy of 0.60.
