Automatic Facial Emotion Recognition using Convolutional Neural Networks

The primary aim of this work is to analyze the potential of artificial intelligence in the field of automatic facial emotion recognition (AFER). Therefore, convolutional neural network is considered for classifying the 6 universal facial expressions. The feed-forward artificial neural network is also designed for comparative analysis. The designed techniques are implemented on extended Cohn-Kanade (CK+) database. Rigorous experimentation is carried out in order to analyze the efficacy of the suggested AFER scheme using different performance measures. It is revealed from the analysis that convolutional neural network-based classification proves to be superior in terms of accuracy, precision, recall and F1 score, as compared to the feedforward neural network-based classification scheme.