Review on Facial Emotion Recognition Using Deep Learning With Multiple Databases

Abstract

Facial expression-based automatic emotion recognition is an intriguing field of study that has been presented and used in a variety of contexts, including human-machine interfaces, safety, and health. In order to improve computer predictions, researchers in this field are interested in creating methods for interpreting, coding, and extracting facial expressions. Deep learning has been incredibly successful, and as a result, its various architectures are being used to improve performance. This paper aims to investigate recent advances in deep learning-based automatic facial emotion recognition (FER). We highlight the contributions addressed, the architecture, and the databases employed. We also demonstrate the advancement by contrasting the suggested approaches with the outcomes attained. This paper aims to assist and direct researchers by reviewing current literature and offering perspectives to advance this field.