Facial Emotion Recognition using Convolutional Neural Networks

TL;DR

The goal of this paper is to classify images of human faces into one of seven basic emotions, using a final Convolutional Neural Network (CNN) model.

Abstract

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.

Figures (5)

• Figure 1: FER-2013 Expressions
• Figure 2: FER-2013 Expression Distribution
• Figure 3: Final Model Architecture
• Figure 4: Confusion Matrix for the final model
• Figure 5: Live Testing Module