A Review on Face Emotion Recognition using EEG Features and Facial Features

Emotions recognition using feature extraction from face, Speech and EEG signal have become emerging research field. It has contributed in different research areas like safety, biomedical sector, industrial automation and Human Computer Interface. Deep Learning approach using Convolution Neural Networks (CNN) has provided better results in obtaining an accuracy in Facial Emotion Recognition. Researcher are trying to get better results by using a novel approach along with CNN’s to obtain better results. This paper will help the researchers to understand the novel approach in Deep Learning used for Facial Emotion Recognition (FER) with different techniques and Electroencephalogram (EEG) based emotion recognition classified on basis of Valence, Arousal and Dominance with different datasets like DEAP (Database of Emotion Analysis using Physiological signals), SEED (SJTU Emotion EEG Dataset). The review provided use of different dataset and the accuracy of models while using those dataset along with the novel approaches in Emotion Recognition. This paper also reviews different algorithms, architectures and recent work carried by different researchers.