Facial emotion detection based on improved VGG-16

Abstract

In real life, facial emotion recognition is very important because it can convey information, build relationships, and facilitate communication. Therefore, emotion recognition technology is used in medicine, education, entertainment, security, and other fields. In the emotion detection field, the Facial Emotion Recognition 2013 Dataset (FER-2013) is a dataset that has been used in many places, that contains images of seven emotional expressions. In the area of detecting emotions from facial expressions, the deep learning structures, especially the convolutional neural networks (CNNs), have demonstrated significant potential since they have the ability to extract features and their computational efficiency. In this paper, the author constructs a model named Improved VGG-16 based on Visual Geometry Group Network of 16 weight layers (VGG-16). To be specific, first, the author adds two dense layers to improve the complexity and expressiveness; second, two dropout layers are used in order to reduce overfitting. An accuracy of 68.0% is achieved by this model on the test dataset of FER-2013. The result is better than some previous methods and shows that the improved VGG-16 model can recognize facial expressions effectively. In conclusion, this work aims to increase the accuracy and reliability of facial emotion recognition, providing support for research and application in related fields.