Facial Expression Recognition Using a Simplified Convolutional Neural Network Model

Facial Expression Recognition (FER) is one of the most important information channels by which Human-Computer Interaction (HCI) systems can recognize human emotions. The importance of FER is not limited to the direct interaction between the machine and humans but can be extended to security, virtual reality, education, and entertainment. In this paper, we propose two Convolutional Neural Network (CNN) models for FER. One of these models achieved 100% accuracy for the JAFFE and CK+ benchmark datasets with lower computational complexity. We applied image augmentation techniques and image enhancement techniques with the first model. The other CNN model is an extended version of the first model that h as been validated for t he more challenging FER2013 dataset and we obtained 69.32% for this dataset. By comparing to the recent state-of-the-art approaches to FER, we demonstrate the superior accuracy and efficiency of the proposed approaches.