Facial emotion classification based on the modified VGG and MobileNet

Abstract

Emotion classification is an essential area of face recognition, which is widely applied recently, especially in information security. This study focus on model performance to explore a more efficient one that can produce a more precise prediction. In this paper, this study implement two distinct models, which are modified to classify emotions. The adaption models are added by one convolutional layer, one flatten layer, followed by activation layer and loss function layer. In order to estimate and compare them more straightforwardly, the metrics such as precision, loss, accuracy, recall and auc are introduced. Also, data augmentation to enlarge data sample size is achieved by rotation, width and length shift as well as horizontal lift. In the end, VGG16 shows a better performance with 0.9238 accuracy and 0.7780 loss in training, whereas MobileNet is slightly less outstanding with 0.9009 accuracy and 1.0280 loss in training. This study deal with choices of specific emotion classification models and attain a conclusion that VGG16 performs better in the given dataset.