Automated emotion recognition: Current trends and future perspectives

M Maithri; U Raghavendra; Anjan Gudigar; Jyothi Samanth; Prabal Datta Barua; Murugappan Murugappan; Yashas Chakole; U Rajendra Acharya

doi:10.1016/j.cmpb.2022.106646

Automated emotion recognition: Current trends and future perspectives

Comput Methods Programs Biomed. 2022 Mar:215:106646. doi: 10.1016/j.cmpb.2022.106646. Epub 2022 Jan 19.

Authors

M Maithri¹, U Raghavendra², Anjan Gudigar³, Jyothi Samanth⁴, Prabal Datta Barua⁵, Murugappan Murugappan⁶, Yashas Chakole², U Rajendra Acharya⁷

Affiliations

¹ Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
² Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
³ Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India. Electronic address: anjan.gudigar@manipal.edu.
⁴ Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka 576104, India.
⁵ School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
⁶ Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, 13133, Kuwait.
⁷ School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.

PMID: 35093645
DOI: 10.1016/j.cmpb.2022.106646

Abstract

Background: Human emotions greatly affect the actions of a person. The automated emotion recognition has applications in multiple domains such as health care, e-learning, surveillance, etc. The development of computer-aided diagnosis (CAD) tools has led to the automated recognition of human emotions.

Objective: This review paper provides an insight into various methods employed using electroencephalogram (EEG), facial, and speech signals coupled with multi-modal emotion recognition techniques. In this work, we have reviewed most of the state-of-the-art papers published on this topic.

Method: This study was carried out by considering the various emotion recognition (ER) models proposed between 2016 and 2021. The papers were analysed based on methods employed, classifier used and performance obtained.

Results: There is a significant rise in the application of deep learning techniques for ER. They have been widely applied for EEG, speech, facial expression, and multimodal features to develop an accurate ER model.

Conclusion: Our study reveals that most of the proposed machine and deep learning-based systems have yielded good performances for automated ER in a controlled environment. However, there is a need to obtain high performance for ER even in an uncontrolled environment.

Keywords: CAD; Electroencephalogram (EEG); Facial; Human emotions; Machine learning; Voice.

Publication types

Review

MeSH terms

Electroencephalography*
Emotions
Facial Expression*
Humans
Speech