Emotion Recognition from Facial Expression Using Deep Learning Model : A Review

Emotion recognition is a well-known and extensively studied subject having applications in many scientific and busi-ness domains. In order to categorise expressions like happiness, sadness, contempt, surprise, fear, and wrath, it entails extracting facial elements from static facial photos. People communicate non verbally by using their facial expressions to transmit information to one another. It is essential to recognise the emotions on people's faces, and efficient techniques like (MTCNN, SVM, HAAR classifier, SIFT, MLP) are required to do this. This article aims to investigate current approaches for Facial Emotion Recognition (FER), offering an in-depth review of the approaches used by researchers to analyse facial emotions. Due to the variety of methods used to convey resemblance and the impact of the selected dataset like (JAFFE, FER2013, CK+, The Yale Face Database B, LFW) on the outcomes, comparing various facial emotions becomes challenging.