Performance Analysis and Evaluation of Different Deep Learning Algorithms for Facial Expression Recognition

Abstract

Emotions are dynamic biological states that are connected to all of the nerve systems. The problem of facial expression recognition has been thoroughly investigated, leading to the development of some robust and accurate face recognition algorithms. The effectiveness of three such algorithms (CNN, VGG16, and ResNet50) that have been widely studied and applied in the research community are investigated and compared in this paper. The aim is to use grayscale images to train these training models and compare their accuracy and data losses. The system will be able to detect the seven facial expressions Angry, Neutral, Contempt, Disgust, Fear, Happy, and Sad after training these models. To compare their precision, the same batch size and epoch were used. After reviewing all possible evaluations based on these output matrices, it is clear that all three networks produce reliable effect identification, with CNN being the most accurate.