Recent trends and challenges in human computer interaction using automatic emotion recognition: a review

Automatic emotion recognition (AER) using facial expressions and electroencephalogram (EEG) signals is an interesting and booming area of research in the field of human computer interaction This paper aims to identify the key state-of-the-art methodologies, understand the standard workflow pipeline and know the existing findings. Different machine learning and deep learning approaches used recently for information pre-processing, feature extraction, feature classification and fusion schemes have also been explored. Furthermore, the purpose of this review work is to discuss the aspects motivating researchers to move from unimodal to multimodal AER systems. Also, this surveyed information is summarised in tabular forms to investigate the recent methods used and the results obtained. This comprehensive literature survey identifies the key points for inclusion of facial expressions and EEG signals over other channels, also the benefits of automated features, which are being leveraged over hand crafted features for building improved real time emotion recognition systems. This review work provides new research directions, open challenges and existing state-of-the-art methods in the field of AER using facial expressions and EEG signals which can be used as benchmark studies for researchers.